Submision_Movie_Recomendation_Update_Dua.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tE3RPwN3YcQr"
      },
      "source": [
        "# PROYEK REKOMENDASI FILM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9ykDYqLY0dw"
      },
      "source": [
        "- Nama: [Maklon Jaco Frare] <br>\n",
        "- Email: [maklonjacob.frare@gmail.com] <br>\n",
        "- ID Dicoding: [maklon]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlguKCZ_Z6D0"
      },
      "source": [
        "# DATA LOADING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0njKihMZsPS"
      },
      "source": [
        "## Import Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gEZlwLNHYbXB"
      },
      "outputs": [],
      "source": [
        "import shutil\n",
        "import os\n",
        "import zipfile\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "from ast import literal_eval\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Embedding, Flatten, Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import layers\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.models import Sequential\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import missingno as msno\n",
        "from wordcloud import WordCloud\n",
        "%matplotlib inline\n",
        "\n",
        "from google.colab import files\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.utils import shuffle\n",
        "from scipy.sparse import csr_matrix  # Untuk struktur sparse\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YEbHKNhaK1X"
      },
      "source": [
        "## Download Dataset dan Extrak Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msrhu5Sld1z4"
      },
      "source": [
        "Pada tahap, kita akan mengambil dataset dari luar (Kaggle) yakni Dataset The Movies Dataset yang diupload oleh\n",
        "Rounak Banik. Datasetnya dapat dilihat pada [tautan ini](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rjVPWiUMcRnM"
      },
      "outputs": [],
      "source": [
        "#!kaggle datasets download -d rounakbanik/the-movies-dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONhe0OAseLAo"
      },
      "source": [
        "Selanjutnya, kita akan mengekstrak file zip dan mengambil dataset dalam format csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A7yGIpxIeVbU"
      },
      "outputs": [],
      "source": [
        "#!unzip \"the-movies-dataset.zip\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1EzL2urenqq"
      },
      "source": [
        "## Copy Dataset File to Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IHqYvyIe7O-"
      },
      "source": [
        "Pada tahap ini, kita akan menyalin file csv yang akan diolah. Pada proyek ini kita hanya menggunakan file `movies_metadata.csv` dan `rating.csv`. Langkah awal kita hubungankan colab ke drive kita dengan fungsi `mount`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9egwbiHfksP",
        "outputId": "1aa7f4e8-1da0-4007-82fd-4d7134c2dab4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# impor library drive untuk salin data ke drive kita\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5RsPHNAgra3"
      },
      "source": [
        "Selanjutnya, kita salin file `mivies_metadata.csv` dan `ratings.csv` dari folder content google colab ke folder MoviesDataset google drive kita"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vM-jSo5fg3kd"
      },
      "outputs": [],
      "source": [
        "#!cp {\"/content/movies_metadata.csv\"} {\"/content/drive/MyDrive/Dataset/MoviesDataset\"}\n",
        "#!cp {\"/content/ratings_small.csv\"} {\"/content/drive/MyDrive/Dataset/MoviesDataset\"}\n",
        "#!cp {\"/content/ratings.csv\"} {\"/content/drive/MyDrive/Dataset/MoviesDataset\"}\n",
        "#!cp {\"/content/credits.csv\"} {\"/content/drive/MyDrive/Dataset/MoviesDataset\"}\n",
        "#!cp {\"/content/keywords.csv\"} {\"/content/drive/MyDrive/Dataset/MoviesDataset\"}\n",
        "#!cp {\"/content/links.csv\"} {\"/content/drive/MyDrive/Dataset/MoviesDataset\"}\n",
        "#!cp {\"/content/links_small.csv\"} {\"/content/drive/MyDrive/Dataset/MoviesDataset\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DYgZcZKjshz"
      },
      "source": [
        "## Reading Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4IC-C1WjwDn"
      },
      "source": [
        "Pada tahap ini kita akan membaca dataset kita yang sudah tersalin pada drive kita"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g3bcyXSfj3OH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65ed52cd-78fa-4a8d-ee80-60d984cb8406"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah File Dataset Sebanyak: 7\n"
          ]
        }
      ],
      "source": [
        "# Path folder yang ingin dihitung\n",
        "folder_path = \"/content/drive/MyDrive/Dataset/MoviesDataset\"\n",
        "\n",
        "# Menghitung jumlah file di dalam folder\n",
        "file_count = len([f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))])\n",
        "\n",
        "# Menghitung jumlah folder di dalam folder\n",
        "folder_count = len([f for f in os.listdir(folder_path) if os.path.isdir(os.path.join(folder_path, f))])\n",
        "\n",
        "print(f\"Jumlah File Dataset Sebanyak: {file_count}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# menyimpan dataset dalam variabel\n",
        "movies = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/movies_metadata.csv', low_memory=False)\n",
        "ratings = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/ratings_small.csv', low_memory=False)\n",
        "ratings_all = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/ratings.csv', low_memory=False)\n",
        "keywords = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/keywords.csv', low_memory=False)\n",
        "credits = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/credits.csv', low_memory=False)\n",
        "links = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/links.csv', low_memory=False)\n",
        "links_small = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/links_small.csv', low_memory=False)"
      ],
      "metadata": {
        "id": "VVEQteKFLRYf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('----------------------------------------------------------------------------------------------------------------------')\n",
        "print('Jumlah data film sebanyak: ', len(movies.id.unique()))\n",
        "print('----------------------------------------------------------------------------------------------------------------------')\n",
        "print('Jumlah data rating yang diberikan pengguna pada semua file rating (ratings.csv) sebanyak: ', len(ratings_all))\n",
        "print('Jumlah data pengguna yang memberikan rating pada semua file rating (ratings.csv) sebanyak: ', len(ratings_all.userId.unique()))\n",
        "print('Jumlah data film yang diberikan rating oleh pengguna pada semua file rating (ratings.csv) sebanyak: ', len(ratings_all.movieId.unique()))\n",
        "\n",
        "print('----------------------------------------------------------------------------------------------------------------------')\n",
        "print('Jumlah data rating pada sebagian file rating (ratings_small.csv) sebanyak: ', len(ratings))\n",
        "print('Jumlah data pengguna yang memberikan rating pada sebagian file rating (ratings_all.csv) sebanyak: ', len(ratings.userId.unique()))\n",
        "print('Jumlah data film yang diberikan rating oleh pengguna pada sebagian file rating (ratings_all.csv)  sebanyak: ', len(ratings.movieId.unique()))\n",
        "\n",
        "print('----------------------------------------------------------------------------------------------------------------------')\n",
        "print('Jumlah data kata kunci sebanyak: ',len(keywords.id.unique()))\n",
        "print('----------------------------------------------------------------------------------------------------------------------')\n",
        "print('Jumlah data pemeran dan kru untuk semua film sebanyak: ', len(credits.id.unique()))\n",
        "print('----------------------------------------------------------------------------------------------------------------------')\n",
        "print('Jumlah data ID TMDB dan IMDB dari semua film sebanyak: ', len(links.movieId.unique()))\n",
        "print('----------------------------------------------------------------------------------------------------------------------')\n",
        "print('Jumlah data ID TMDB dan IMDB dari sebagian kecil film dari Kumpulan Data Lengkap sebanyak: ', len(links_small.movieId.unique()))\n",
        "print('----------------------------------------------------------------------------------------------------------------------')"
      ],
      "metadata": {
        "id": "nJChdqb3LUi0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5750d6ca-2ae4-4a81-8938-f3c2f5db0f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------------------------\n",
            "Jumlah data film sebanyak:  45436\n",
            "----------------------------------------------------------------------------------------------------------------------\n",
            "Jumlah data rating yang diberikan pengguna pada semua file rating (ratings.csv) sebanyak:  26024289\n",
            "Jumlah data pengguna yang memberikan rating pada semua file rating (ratings.csv) sebanyak:  270896\n",
            "Jumlah data film yang diberikan rating oleh pengguna pada semua file rating (ratings.csv) sebanyak:  45115\n",
            "----------------------------------------------------------------------------------------------------------------------\n",
            "Jumlah data rating pada sebagian file rating (ratings_small.csv) sebanyak:  100004\n",
            "Jumlah data pengguna yang memberikan rating pada sebagian file rating (ratings_all.csv) sebanyak:  671\n",
            "Jumlah data film yang diberikan rating oleh pengguna pada sebagian file rating (ratings_all.csv)  sebanyak:  9066\n",
            "----------------------------------------------------------------------------------------------------------------------\n",
            "Jumlah data kata kunci sebanyak:  45432\n",
            "----------------------------------------------------------------------------------------------------------------------\n",
            "Jumlah data pemeran dan kru untuk semua film sebanyak:  45432\n",
            "----------------------------------------------------------------------------------------------------------------------\n",
            "Jumlah data ID TMDB dan IMDB dari semua film sebanyak:  45843\n",
            "----------------------------------------------------------------------------------------------------------------------\n",
            "Jumlah data ID TMDB dan IMDB dari sebagian kecil film dari Kumpulan Data Lengkap sebanyak:  9125\n",
            "----------------------------------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan varibel-variabel dataset di ataas, kita cukup mengambil variabel sesuai kebutuhan analisis dan pelatihan model pada proyek ini yakni movies dan rating."
      ],
      "metadata": {
        "id": "-J_oBP_FNpx4"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvNNXZXK7HYI"
      },
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Univariate Analysis"
      ],
      "metadata": {
        "id": "_-ywF94YRVbP"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SId2Mh_i2G-"
      },
      "source": [
        "Pada tahap ini kita akan melihat variabel dataset serta menggunakan grafik untuk menggambarkan distribusi genre dan rating film, serta hubungan antara fitur-fitur dalam dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9vdVwE47L9A"
      },
      "source": [
        "### Deskripsi Variabel"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada langkah ini kita akan menampilkan informasi variabel ratings dan movies dengan fungsi `info`. Pertama kita mulai cek variabel ratings"
      ],
      "metadata": {
        "id": "pSJrd-aBLznP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings.info()"
      ],
      "metadata": {
        "id": "SFM-QSJZL3Q1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92150563-07e2-4eee-efd1-b100b51d0c75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100004 entries, 0 to 100003\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   userId     100004 non-null  int64  \n",
            " 1   movieId    100004 non-null  int64  \n",
            " 2   rating     100004 non-null  float64\n",
            " 3   timestamp  100004 non-null  int64  \n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 3.1 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUF9UP2z7OxZ"
      },
      "source": [
        "Berdasarkan gambar diatas, variabel ratings terdiri dari 100004 baris dan 4 kolom yang dapat dijelaskan sebagai berikut:\n",
        "\n",
        "| Variabel                    | Keterangan     |\n",
        "| -----------------------     | ------------------------------------------------------------------------- |\n",
        "| userId                      | ID unik untuk pengguna yang memberikan penilaian (rating). Ini digunakan untuk mengidentifikasi pengguna secara anonim.  |\n",
        "| movieId|ID unik untuk film yang dinilai oleh pengguna untuk mendapatkan informasi lebih detail tentang film tersebut.                                  |\n",
        "| rating | Nilai yang diberikan oleh pengguna untuk film tertentu dengan skala 1 hingga 5, di mana angka yang lebih tinggi menunjukkan penilaian yang lebih positif. |\n",
        "| timestamp                   | Waktu ketika penilaian diberikan, direpresentasikan dalam format UNIX timestamp (jumlah detik sejak 1 Januari 1970).   |"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kedua kita cek variabel movies"
      ],
      "metadata": {
        "id": "RAgjJLmtMGp6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies.info()"
      ],
      "metadata": {
        "id": "N-GmplQPMHHl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90f42b54-00bf-4670-9b18-a2b409db7486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45466 entries, 0 to 45465\n",
            "Data columns (total 24 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   adult                  45466 non-null  object \n",
            " 1   belongs_to_collection  4494 non-null   object \n",
            " 2   budget                 45466 non-null  object \n",
            " 3   genres                 45466 non-null  object \n",
            " 4   homepage               7782 non-null   object \n",
            " 5   id                     45466 non-null  object \n",
            " 6   imdb_id                45449 non-null  object \n",
            " 7   original_language      45455 non-null  object \n",
            " 8   original_title         45466 non-null  object \n",
            " 9   overview               44512 non-null  object \n",
            " 10  popularity             45461 non-null  object \n",
            " 11  poster_path            45080 non-null  object \n",
            " 12  production_companies   45463 non-null  object \n",
            " 13  production_countries   45463 non-null  object \n",
            " 14  release_date           45379 non-null  object \n",
            " 15  revenue                45460 non-null  float64\n",
            " 16  runtime                45203 non-null  float64\n",
            " 17  spoken_languages       45460 non-null  object \n",
            " 18  status                 45379 non-null  object \n",
            " 19  tagline                20412 non-null  object \n",
            " 20  title                  45460 non-null  object \n",
            " 21  video                  45460 non-null  object \n",
            " 22  vote_average           45460 non-null  float64\n",
            " 23  vote_count             45460 non-null  float64\n",
            "dtypes: float64(4), object(20)\n",
            "memory usage: 8.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan gambar diatas, variabel movies terdiri dari 100004 baris dan 24 kolom yang dapat dijelaskan sebagai berikut:\n",
        "\n",
        "| Variabel                    | Keterangan     |\n",
        "| -----------------------     | ------------------------------------------------------------------------- |\n",
        "|adult        | Mengindikasikan apakah film tersebut untuk orang dewasa (adult content). Nilainya biasanya True atau False.|\n",
        "|belongs_to_collection|Informasi tentang koleksi atau seri film tertentu yang mencakup film ini (misalnya, film dalam seri Harry Potter). Biasanya berupa JSON atau string deskriptif.|\n",
        "|Budget|Anggaran produksi film dalam satuan mata uang (biasanya USD). Nilainya berupa angka.|\n",
        "|genres|Daftar genre film, seperti Action, Comedy, Drama. Biasanya berupa JSON atau daftar string.|\n",
        "|homepage|URL dari situs web resmi film tersebut.|\n",
        "|id|ID unik untuk film, biasanya merujuk pada database film tertentu seperti TMDb.|\n",
        "|imdb_id|ID unik dari film di IMDb (misalnya, tt1234567).|\n",
        "|original_language|Bahasa asli film tersebut dalam format kode bahasa ISO 639-1 (misalnya, en untuk bahasa Inggris).|\n",
        "|original_title|Judul asli film dalam bahasa produksinya.|\n",
        "|overview|Judul asli film dalam bahasa produksinya.|\n",
        "|popularity|Skor popularitas film berdasarkan sistem tertentu, sering dihitung menggunakan algoritma dari platform film.|\n",
        "|poster_path|Path atau tautan menuju gambar poster film. Biasanya berupa path yang dapat digabungkan dengan URL dasar untuk akses.|\n",
        "|production_companies|Informasi tentang perusahaan produksi film tersebut. Biasanya berupa JSON dengan nama dan ID perusahaan.|\n",
        "|production_countries|Negara tempat film tersebut diproduksi. Biasanya berupa JSON dengan nama negara dan kode negara.|\n",
        "|release_date|Tanggal rilis film (format: YYYY-MM-DD).|\n",
        "|revenue|Pendapatan kotor yang diperoleh film (biasanya dalam USD).|\n",
        "|runtime|Durasi film dalam menit.|\n",
        "|spoken_languages|Bahasa yang digunakan dalam dialog film. Biasanya berupa JSON dengan nama dan kode bahasa.|\n",
        "|status|Status rilis film (misalnya, Released, In Production).|\n",
        "|tagline|Slogan atau frasa singkat yang biasanya digunakan untuk promosi film.|\n",
        "|title|Judul utama film yang digunakan untuk promosi.|\n",
        "|video|Mengindikasikan apakah ada video terkait film. Nilainya biasanya berupa True atau False.|\n",
        "|vote_average|Nilai rata-rata yang diberikan oleh pengguna (misalnya dari IMDb atau TMDb) berdasarkan skala tertentu (biasanya 1-10).|\n",
        "|vote_count|Jumlah suara atau ulasan yang diberikan untuk film tersebut.|"
      ],
      "metadata": {
        "id": "mXkuGAu3MJ8E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kedua gambar diatas merupakan informasi variabel movies dan rating dataset kita."
      ],
      "metadata": {
        "id": "lZfHqq7mMQeR"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMUkWOAOabYz"
      },
      "source": [
        "### Melihat Infromasi Tipe Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQCgW_7cafJy"
      },
      "source": [
        "Pada tahap ini, kita akan melihat informasi pada dataset *ratings* dan *movies*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "6808wVAX7nVT",
        "outputId": "1b14f37c-0868-4aa4-d7ba-cc4420988e17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "Name: count, dtype: int64"
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>object</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>float64</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "tipe_data_movies = movies.dtypes.value_counts()\n",
        "tipe_data_movies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "7lfaojvN7rx6",
        "outputId": "6f186403-c489-49d0-abe8-c0bb26fa02ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int64      3\n",
              "float64    1\n",
              "Name: count, dtype: int64"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>int64</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>float64</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tipe_data_ratings = ratings.dtypes.value_counts()\n",
        "tipe_data_ratings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyT8KoiDKKos"
      },
      "source": [
        "Dapat dilihat pada informasi dataset **movies** 20 variable dengan tipe data object dan 4 variabel bertipe float64. Sedangkan pada informasi dataset **ratings** terdapat 1 variabel dengan tipe data float64 dan 3 variable dengan tipe data int64."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGKJvA56ZYSQ"
      },
      "source": [
        "### Menghitung Total Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7uZDhWVHPDw"
      },
      "source": [
        "Pertama, kita akan melihat total dataset `ratings` dan `movies`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZFLNj3BTHZWO",
        "outputId": "8d91fce0-dae3-430e-8ead-bf2f19a89629"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah data Movie sebanyak 45466, dan memiliki 24 kolom\n",
            "Jumlah data Rating sebanyak 100004, dan memiliki 4 kolom\n"
          ]
        }
      ],
      "source": [
        "rows_movie = movies.shape[0]\n",
        "cols_movie = movies.shape[1]\n",
        "rows_rating = ratings.shape[0]\n",
        "cols_rating = ratings.shape[1]\n",
        "\n",
        "print(f'Jumlah data Movie sebanyak {rows_movie}, dan memiliki {cols_movie} kolom')\n",
        "print(f'Jumlah data Rating sebanyak {rows_rating}, dan memiliki {cols_rating} kolom')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1a6hD420HvjU"
      },
      "source": [
        "### Menghitung Total Data Unik"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0KJD5vzHzDw",
        "outputId": "acfb553a-3095-4f9a-a864-17839fe3dd62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah film unik pada dataset movies : 45436\n",
            "Jumlah film unik pada dataset ratings: 9066\n",
            "Jumlah user unik pada dataset ratings: 671\n"
          ]
        }
      ],
      "source": [
        "print(f\"Jumlah film unik pada dataset movies : {movies['id'].nunique()}\")\n",
        "print(f\"Jumlah film unik pada dataset ratings: {ratings['movieId'].nunique()}\")\n",
        "print(f\"Jumlah user unik pada dataset ratings: {ratings['userId'].nunique()}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxjkGtiqasTH"
      },
      "source": [
        "Dari hasil diatas terdapat 45436 film pada dataset movies, 9066 film pada dataset ratings dan 671 user pada dataset ratings"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Melihat Data Deskriptif pada variabel dataset"
      ],
      "metadata": {
        "id": "2dL7W3hQv26q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "m5GC3mDhJwyF",
        "outputId": "fbf03e25-d5e5-464f-e030-01b9c12a90f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            revenue       runtime  vote_average    vote_count\n",
              "count  4.546000e+04  45203.000000  45460.000000  45460.000000\n",
              "mean   1.120935e+07     94.128199      5.618207    109.897338\n",
              "std    6.433225e+07     38.407810      1.924216    491.310374\n",
              "min    0.000000e+00      0.000000      0.000000      0.000000\n",
              "25%    0.000000e+00     85.000000      5.000000      3.000000\n",
              "50%    0.000000e+00     95.000000      6.000000     10.000000\n",
              "75%    0.000000e+00    107.000000      6.800000     34.000000\n",
              "max    2.787965e+09   1256.000000     10.000000  14075.000000"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 982127592.5923291,\n        \"min\": 0.0,\n        \"max\": 2787965087.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11209348.539265288,\n          2787965087.0,\n          64332246.740631126\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15902.502664555748,\n        \"min\": 0.0,\n        \"max\": 45203.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          94.12819945578833,\n          95.0,\n          45203.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16070.752359182798,\n        \"min\": 0.0,\n        \"max\": 45460.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.618207215134185,\n          6.0,\n          45460.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16090.013060419962,\n        \"min\": 0.0,\n        \"max\": 45460.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          109.89733831940167,\n          10.0,\n          45460.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "movies.describe()"
      ]
    },
    {
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      "execution_count": null,
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "APF9fNY9KBnY",
        "outputId": "050c56d7-555f-4ea6-c2f5-c2a7b29bbb57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              userId        movieId         rating     timestamp\n",
              "count  100004.000000  100004.000000  100004.000000  1.000040e+05\n",
              "mean      347.011310   12548.664363       3.543608  1.129639e+09\n",
              "std       195.163838   26369.198969       1.058064  1.916858e+08\n",
              "min         1.000000       1.000000       0.500000  7.896520e+08\n",
              "25%       182.000000    1028.000000       3.000000  9.658478e+08\n",
              "50%       367.000000    2406.500000       4.000000  1.110422e+09\n",
              "75%       520.000000    5418.000000       4.000000  1.296192e+09\n",
              "max       671.000000  163949.000000       5.000000  1.476641e+09"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100004.000000</td>\n",
              "      <td>100004.000000</td>\n",
              "      <td>100004.000000</td>\n",
              "      <td>1.000040e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>347.011310</td>\n",
              "      <td>12548.664363</td>\n",
              "      <td>3.543608</td>\n",
              "      <td>1.129639e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>195.163838</td>\n",
              "      <td>26369.198969</td>\n",
              "      <td>1.058064</td>\n",
              "      <td>1.916858e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>7.896520e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>182.000000</td>\n",
              "      <td>1028.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.658478e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>367.000000</td>\n",
              "      <td>2406.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.110422e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>520.000000</td>\n",
              "      <td>5418.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.296192e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>671.000000</td>\n",
              "      <td>163949.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.476641e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e01027a7-2dfd-482c-aa91-2f0ab0759b1c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e01027a7-2dfd-482c-aa91-2f0ab0759b1c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e94d0d4e-d453-4ea9-9138-8f31b3c7cbe7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e94d0d4e-d453-4ea9-9138-8f31b3c7cbe7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ratings\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35242.04814425726,\n        \"min\": 1.0,\n        \"max\": 100004.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          347.0113095476181,\n          367.0,\n          100004.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60500.96948804269,\n        \"min\": 1.0,\n        \"max\": 163949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12548.664363425463,\n          2406.5,\n          100004.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35355.68751044932,\n        \"min\": 0.5,\n        \"max\": 100004.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          100004.0,\n          3.543608255669773,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 521994354.75508785,\n        \"min\": 100004.0,\n        \"max\": 1476640644.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1129639086.9392424,\n          1110421822.0,\n          100004.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "ratings.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0_YIXXeKS89"
      },
      "source": [
        "Berdasarkan tampilan deskriptif dataset movies dan ratings dapat dilihat tidak mencolok ada pesebaran nilai yang menimbulkan `outlier`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hrBvKE6N20b"
      },
      "source": [
        "### Distribusi Ratings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZF8mvuEOmp2"
      },
      "source": [
        "Pada langkah ini kita akan mendistribusi ratings dengan tujuan:\n",
        "1. Mengidentifikasi nilai rating yang paling umum diberikan oleh pengguna.\n",
        "2. Menilai apakah data rating cenderung condong ke satu nilai (misalnya, lebih banyak rating tinggi atau rendah).\n",
        "3. Membantu memahami pola preferensi pengguna."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "DDsWvjm8N3DS",
        "outputId": "a3e5be2e-8b95-4950-f4bf-efec3f393bc2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Presentasi rating')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Mengelompokkan data berdasarkan kolom rating dan menghitung jumlah baris (count) untuk setiap nilai rating\n",
        "count_rates = ratings.groupby('rating').count()\n",
        "\n",
        "#Menghitung persentase jumlah rating tertentu terhadap total jumlah rating.\n",
        "count_rates['percents_total']=round(count_rates['userId']*100/count_rates['userId'].sum(),1)\n",
        "\n",
        "# Memvisualisasikan kolom perc_total dalam bentuk diagram batang menggunakan fungsi plot.bar()\n",
        "# Membuat diagram batang\n",
        "ax = count_rates['percents_total'].plot.bar(color='#0A3981', figsize=(8, 5))\n",
        "\n",
        "# Menambahkan label persentase pada batang\n",
        "for index, value in enumerate(count_rates['percents_total']):\n",
        "    plt.text(index, value + 0.5, f'{value}%', ha='center', fontsize=10)\n",
        "\n",
        "# Memberikan label pada sumbu\n",
        "plt.xlabel('Rating', fontsize=12)\n",
        "plt.ylabel('Presentasi (%)', fontsize=12)\n",
        "plt.title('Presentasi rating', fontsize=14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBQtDt_9RCLb"
      },
      "source": [
        "Berdasarkan diagram plot rating diatas, dapat dilihat bahwa nilai ratings paling umum diberikan pengguna adalah rating 4.0 dengan presentasi 28.7%, rating 3.0 dengan presentasi 20.1%, rating 5.0 dengan prestansi 15.1%. Sedangkan nilai rating yang lain berada dibawah pada presentasi 12.0%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJXbipyZSLXV"
      },
      "source": [
        "### Distribusi Genres"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QxwMpm-eEYpO"
      },
      "source": [
        "Pada tahap ini kita akan membersihkan, memproses, dan menormalkan data dalam kolom genres pada DataFrame df_movies. Ada beberapa fungsi yang kita pakai yakni:\n",
        "* `fillna('[]')`, berfungsi untuk mengisi nilai`null` atau `NaN` dalam kolom genres dengan string kosong dalam format `list` ('[]').\n",
        "* `apply(literal_eval)`, fungsi `literal_eval` dari pustaka `ast` untuk mengubah string yang terlihat seperti Python literal menjadi tipe data `list`.\n",
        "* `apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else []`,\n",
        "fungsi lambda ini memproses setiap nilai dalam kolom genres:\n",
        "Jika nilai adalah sebuah daftar `(isinstance(x, list))`, maka ambil nilai dari kunci `name` untuk setiap elemen. Jika nilai bukan daftar, mengembalikan daftar kosong (`[]`)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VirQlyAn1DvL"
      },
      "source": [
        "Langkah pertama kita buat variabel dataframe baru untuk melakukan analisis visualisasi data. Kemudian kita konversi fitur(variabel) genres ke dalam bentuk list sehingga dapat dianalisis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eW6QlcpEY4e"
      },
      "outputs": [],
      "source": [
        "df_analisis_movies = movies.copy()\n",
        "df_analisis_movies['genres'] = df_analisis_movies['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlZ6XTfVJqOU"
      },
      "source": [
        "Selanjutanya kita ubah setiap elemen dalam daftar (genre) menjadi baris terpisah dengan fungsi `explode()`, kemudian menghitung jumlah kemunculan setiap genre dengan fungsi `value_counts()` dan terakhir  kita membuat diagram batang untuk menampilkan distribusi genre dengan plot bar `plot(kind='bar')`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CuXnEdkTGBqH",
        "outputId": "36d06775-5bed-4a78-8d50-ec98544c6502"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribusi Genre:\n",
            "genres\n",
            "Drama                                    20265\n",
            "Comedy                                   13182\n",
            "Thriller                                  7624\n",
            "Romance                                   6735\n",
            "Action                                    6596\n",
            "Horror                                    4673\n",
            "Crime                                     4307\n",
            "Documentary                               3932\n",
            "Adventure                                 3496\n",
            "Science Fiction                           3049\n",
            "Family                                    2770\n",
            "Mystery                                   2467\n",
            "Fantasy                                   2313\n",
            "Animation                                 1935\n",
            "Foreign                                   1622\n",
            "Music                                     1598\n",
            "History                                   1398\n",
            "War                                       1323\n",
            "Western                                   1042\n",
            "TV Movie                                   767\n",
            "Carousel Productions                         1\n",
            "Vision View Entertainment                    1\n",
            "Telescene Film Group Productions             1\n",
            "Aniplex                                      1\n",
            "GoHands                                      1\n",
            "BROSTA TV                                    1\n",
            "Mardock Scramble Production Committee        1\n",
            "Sentai Filmworks                             1\n",
            "Odyssey Media                                1\n",
            "Pulser Productions                           1\n",
            "Rogue State                                  1\n",
            "The Cartel                                   1\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Menghitung distribusi genre\n",
        "all_genres = df_analisis_movies['genres'].explode()  # Mengubah elemen dalam list menjadi baris terpisah\n",
        "genre_counts = all_genres.value_counts()  # Menghitung jumlah tiap genre\n",
        "\n",
        "# Menampilkan hasil distribusi\n",
        "print(\"Distribusi Genre:\")\n",
        "print(genre_counts)\n",
        "\n",
        "# Gradasi warna dengan cmap\n",
        "colors = plt.cm.viridis(np.linspace(0, 1, len(genre_counts)))\n",
        "\n",
        "# Plot distribusi genre\n",
        "plt.figure(figsize=(18, 6))\n",
        "genre_counts.plot(kind='bar', color=colors)\n",
        "plt.title('Distribusi dari Genre', fontsize=14)\n",
        "plt.xlabel('Genre', fontsize=12)\n",
        "plt.ylabel('Frekuensi', fontsize=12)\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYr5ZjVFL0OG"
      },
      "source": [
        "Dari grafik diatas, dapat dilihat bahwa genre Drama dan Comedy paling banyak tersebar pada setiap film dalam dataset dengan jumah sebesar 20265 dan 13182. Sedangkan genre yang lain berada dibawah 10000. Terlihat juga ada 12 genre dengan jumlah 1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sDJ4NpuRBKK"
      },
      "source": [
        "### Analisis Daftar film dengan skor tertinggi di seluruh rentang film"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lKqCnG8YXKq"
      },
      "source": [
        "Untuk membuat daftar film dengan skor tertinggi menggunakan metode `Weighted Score`. Metode ini merupakan perhitungan skor berbobot untuk menggabungkan nilai-nilai yang berbeda berdasarkan pentingnya masing-masing komponen. Dalam konteks film, kita perlu menghitung skor berbobot berdasarkan informasi yang tersedia, seperti rata-rata penilaian (`vote_average`), jumlah suara (`vote_count`), dan jumlah suara rata-rata minimum yang diperlukan untuk dipertimbangkan dalam daftar.\n",
        "Keterangan:\n",
        "* v = jumlah suara untuk film tertentu (`vote_count`)\n",
        "* m = jumlah suara minimum untuk masuk ke daftar (`threshold`)\n",
        "* R = rata-rata skor film tersebut (`vote_average`)\n",
        "* C = rata-rata skor semua film dalam dataset (`rata-rata global`)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "USivrc4BRBYS",
        "outputId": "28251002-8484-48c6-b9a1-1b1085113a9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Daftar 5 Film dengan Skor Tertinggi:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           id                            genres                        title  \\\n",
              "10309   19404          [Comedy, Drama, Romance]  Dilwale Dulhania Le Jayenge   \n",
              "314       278                    [Drama, Crime]     The Shawshank Redemption   \n",
              "834       238                    [Drama, Crime]                The Godfather   \n",
              "40251  372058       [Romance, Animation, Drama]                   Your Name.   \n",
              "12481     155  [Drama, Action, Crime, Thriller]              The Dark Knight   \n",
              "\n",
              "       vote_average  vote_count  weighted_score  \n",
              "10309           9.1       661.0        8.929668  \n",
              "314             8.5      8358.0        8.488324  \n",
              "834             8.5      6024.0        8.483826  \n",
              "40251           8.5      1030.0        8.407913  \n",
              "12481           8.3     12269.0        8.292589  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a3ce6418-e2f5-4a55-b684-36485180d916\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>genres</th>\n",
              "      <th>title</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>weighted_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10309</th>\n",
              "      <td>19404</td>\n",
              "      <td>[Comedy, Drama, Romance]</td>\n",
              "      <td>Dilwale Dulhania Le Jayenge</td>\n",
              "      <td>9.1</td>\n",
              "      <td>661.0</td>\n",
              "      <td>8.929668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>278</td>\n",
              "      <td>[Drama, Crime]</td>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8358.0</td>\n",
              "      <td>8.488324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>834</th>\n",
              "      <td>238</td>\n",
              "      <td>[Drama, Crime]</td>\n",
              "      <td>The Godfather</td>\n",
              "      <td>8.5</td>\n",
              "      <td>6024.0</td>\n",
              "      <td>8.483826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40251</th>\n",
              "      <td>372058</td>\n",
              "      <td>[Romance, Animation, Drama]</td>\n",
              "      <td>Your Name.</td>\n",
              "      <td>8.5</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>8.407913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12481</th>\n",
              "      <td>155</td>\n",
              "      <td>[Drama, Action, Crime, Thriller]</td>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>8.3</td>\n",
              "      <td>12269.0</td>\n",
              "      <td>8.292589</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"top_movies[['id', 'genres', 'title', 'vote_average', 'vote_count', 'weighted_score']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"278\",\n          \"155\",\n          \"238\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"The Shawshank Redemption\",\n          \"The Dark Knight\",\n          \"The Godfather\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3033150177620617,\n        \"min\": 8.3,\n        \"max\": 9.1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.1,\n          8.5,\n          8.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4937.538080865807,\n        \"min\": 661.0,\n        \"max\": 12269.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8358.0,\n          12269.0,\n          6024.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24208360671441426,\n        \"min\": 8.292588721882026,\n        \"max\": 8.92966769110009,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.488324481090867,\n          8.292588721882026,\n          8.48382618773763\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Menghitung nilai rata-rata\n",
        "C = df_analisis_movies['vote_average'].mean()\n",
        "R = df_analisis_movies['vote_average']\n",
        "v = df_analisis_movies['vote_count']\n",
        "\n",
        "# Menentukan nilai m (threshold vote_count minimum)\n",
        "m = df_analisis_movies['vote_count'].quantile(0.75)  # Mengambil persentil ke-75 sebagai threshold\n",
        "\n",
        "# Menghitung Weighted Score\n",
        "df_analisis_movies['weighted_score'] = (v / (v + m) * R) + (m / (v + m) * C)\n",
        "\n",
        "# Menyortir berdasarkan Weighted Score\n",
        "top_movies = df_analisis_movies.sort_values('weighted_score', ascending=False)\n",
        "\n",
        "# Menampilkan hasil\n",
        "print(\"Daftar 5 Film dengan Skor Tertinggi:\")\n",
        "#top_movies.head(5)\n",
        "top_movies[['id', 'genres', 'title', 'vote_average', 'vote_count', 'weighted_score']].head(5)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYlebrrpOm0Y"
      },
      "source": [
        "Hasil Daftar tabel diatas menunjukan 5 filim dengan skor tertinggi yang diberikan oleh pengguna."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RfX_lGXectCX"
      },
      "source": [
        "### Melakukan Analisis Rating Tertinggi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZqjyoO_Rikym"
      },
      "source": [
        "Selanjutnya kita gabungkan dataset df_movies dan ratings dengan fungsi pandas `pd.merge` dan mencari 10 film dengan rating tertinggi."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "xyPrMlYncuUM",
        "outputId": "2edca38f-d8c2-446e-fab4-9226404e1ccf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-3f5f448a8240>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  movies_copy['id'] = movies_copy['id'].astype('int64')\n",
            "<ipython-input-21-3f5f448a8240>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  movies_copy.rename(columns={'id': 'movieId'}, inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    mean ratings  total ratings\n",
              "title                                                          \n",
              "Terminator 3: Rise of the Machines      4.256173            324\n",
              "The Million Dollar Hotel                4.487138            311\n",
              "Solaris                                 4.134426            305\n",
              "The 39 Steps                            4.221649            291\n",
              "Monsoon Wedding                         3.706204            274\n",
              "Once Were Warriors                      4.303279            244\n",
              "Three Colors: Red                       3.945175            228\n",
              "Men in Black II                         4.256696            224\n",
              "The Passion of Joan of Arc              3.483945            218\n",
              "Silent Hill                             3.674419            215"
            ],
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-94f4db1f-b650-4ea4-aa41-63754ac6db46 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-94f4db1f-b650-4ea4-aa41-63754ac6db46');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-590c0490-162d-469b-8862-abae48d039f7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-590c0490-162d-469b-8862-abae48d039f7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-590c0490-162d-469b-8862-abae48d039f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_analisis_rating\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"The Passion of Joan of Arc\",\n          \"The Million Dollar Hotel\",\n          \"Once Were Warriors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean ratings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3278939210817524,\n        \"min\": 3.4839449541284404,\n        \"max\": 4.487138263665595,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3.4839449541284404,\n          4.487138263665595,\n          4.30327868852459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total ratings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42,\n        \"min\": 215,\n        \"max\": 324,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          218,\n          311,\n          244\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# membuat salinan varibael dataset untuk proses analisis\n",
        "movies_copy = movies.copy()\n",
        "# Identifikasi dan tangani nilai tidak valid\n",
        "movies_copy = movies_copy[movies['id'].apply(lambda x: str(x).isnumeric())]\n",
        "\n",
        "# Konversi ke tipe int64\n",
        "movies_copy['id'] = movies_copy['id'].astype('int64')\n",
        "# df_movies.head()\n",
        "\n",
        "movies_copy.rename(columns={'id': 'movieId'}, inplace=True)\n",
        "\n",
        "df_movies = movies_copy.copy()\n",
        "\n",
        "rating_movies = pd.merge(ratings, df_movies, on='movieId', how='inner')\n",
        "rating_movies.drop(['timestamp','genres'],axis=1, inplace=True)\n",
        "\n",
        "df_analisis_rating = pd.DataFrame(rating_movies.groupby('title')['rating'].mean())\n",
        "df_analisis_rating['total ratings'] = pd.DataFrame(rating_movies.groupby('title')['rating'].count())\n",
        "df_analisis_rating.rename(columns = {'rating': 'mean ratings'}, inplace=True)\n",
        "\n",
        "df_analisis_rating.sort_values('total ratings', ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqEtUrE0Pynn"
      },
      "source": [
        "Dapat dilihat dari 10 rating tertinggi film yang ada, film dengan judul Terminator 3: Rise of the Machines memiliki rating teratas dengan *mean rating* 4.256 dan total rating sebanyak 324."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9lGu0s8qCkV"
      },
      "source": [
        "### Membandingkan Peringkat rata-rata dan Jumlah total peringkat"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7VGj7JYRKuQ"
      },
      "source": [
        "Pada tahap ini kita akan membandingkan rata-rata rangkin dan total rangking menggunakan `joinplot` untuk melihat pesebaran data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "4NQX4n3KqHfO",
        "outputId": "4683e6c9-d8d0-4efc-a921-e0ef652a4e8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7e46b65c3430>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.jointplot(x = 'mean ratings', y = 'total ratings', data = df_analisis_rating )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ag3g1VOTRhWe"
      },
      "source": [
        "Berdasarkan grafik pesebaran data diatas total rating terting berada diatas 250 sebanyak 5 film, sadangkan rata-rata terbanyak pengguna memberi rating terhadap film berada diretang nilai 2 - 4.5 rating."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlIXfawPAUqe"
      },
      "source": [
        "# DATA PREPARATION"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Clean\n",
        "Proses ini bertujuan untuk menyiapkan data mentah agar dapat digunakan secara efektif dalam model machine learning. Setelah data terkumpul ada beberapa langkah yang perlu lakukan dalam tahap ini yaitu:"
      ],
      "metadata": {
        "id": "Kug70EukwOVF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mengambil Fitur Sesuai Kebutuhan"
      ],
      "metadata": {
        "id": "Fb4zq3PmEL7o"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NesdN2g768QH"
      },
      "source": [
        "Pada tahap ini, kita hanya mengambil beberapa fitur atau kolom dari variabel movies sesuai kebutuhan analsis pengolahan data yakni `['id', 'genres', 'title', 'vote_average', 'vote_count']`. Kemudian kita simpan pada variabel df_movies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "m5VFcr147ApP",
        "outputId": "5dc9d3d0-fca5-4e60-8486-0ac77dc93ff2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                                             genres  \\\n",
              "0    862  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
              "1   8844  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
              "2  15602  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
              "3  31357  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
              "4  11862                     [{'id': 35, 'name': 'Comedy'}]   \n",
              "\n",
              "                         title  vote_average  vote_count  \n",
              "0                    Toy Story           7.7      5415.0  \n",
              "1                      Jumanji           6.9      2413.0  \n",
              "2             Grumpier Old Men           6.5        92.0  \n",
              "3            Waiting to Exhale           6.1        34.0  \n",
              "4  Father of the Bride Part II           5.7       173.0  "
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
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              "      <td>Jumanji</td>\n",
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              "      <td>Grumpier Old Men</td>\n",
              "      <td>6.5</td>\n",
              "      <td>92.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31357</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>6.1</td>\n",
              "      <td>34.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>11862</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>5.7</td>\n",
              "      <td>173.0</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25c94be1-8be3-4d4b-9442-a9247e47b086')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-25c94be1-8be3-4d4b-9442-a9247e47b086 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-25c94be1-8be3-4d4b-9442-a9247e47b086');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
              "\n",
              "<div id=\"df-d34a10d3-37db-4cf5-8f36-2982cc98c11f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d34a10d3-37db-4cf5-8f36-2982cc98c11f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d34a10d3-37db-4cf5-8f36-2982cc98c11f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_movies",
              "summary": "{\n  \"name\": \"df_movies\",\n  \"rows\": 45466,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 45436,\n        \"samples\": [\n          \"274326\",\n          \"156045\",\n          \"87894\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4069,\n        \"samples\": [\n          \"[{'id': 53, 'name': 'Thriller'}, {'id': 16, 'name': 'Animation'}, {'id': 80, 'name': 'Crime'}]\",\n          \"[{'id': 28, 'name': 'Action'}, {'id': 14, 'name': 'Fantasy'}, {'id': 9648, 'name': 'Mystery'}]\",\n          \"[{'id': 10752, 'name': 'War'}, {'id': 53, 'name': 'Thriller'}, {'id': 18, 'name': 'Drama'}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 42277,\n        \"samples\": [\n          \"Far Away\",\n          \"Sully\",\n          \"Z for Zachariah\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9242159915229755,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          3.8,\n          8.1,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 491.3103739397137,\n        \"min\": 0.0,\n        \"max\": 14075.0,\n        \"num_unique_values\": 1820,\n        \"samples\": [\n          5912.0,\n          1588.0,\n          12.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Pilih kolom yang diinginkan\n",
        "selected_features_movies = ['id', 'genres', 'title', 'vote_average', 'vote_count']\n",
        "# Buat DataFrame baru\n",
        "df_movies = movies[selected_features_movies]\n",
        "df_movies.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEJ0apHMCa1d"
      },
      "source": [
        "### Menyesuaikan Tipe Data Primary Key dan Foregein Key"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6vr2kUXehXFI"
      },
      "source": [
        "Jika dilihat pada informasi sebelumnya, dataset movies atribut id (`primary key`) dengan type data `object` berbeda pada dataset ratings atribut movieId dengan type data `int64`. Oleh karena itu,kita perlu menyamakan tipe data tersebut dengan cara mnyamakan nama atribut movieId dan tipe data `int64`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bN3ywQM4hXRz"
      },
      "outputs": [],
      "source": [
        "df_movies_copy = df_movies.copy()\n",
        "# Identifikasi dan tangani nilai tidak valid\n",
        "df_movies_copy = df_movies_copy[df_movies['id'].apply(lambda x: str(x).isnumeric())]\n",
        "\n",
        "# Konversi ke tipe int64\n",
        "df_movies_copy['id'] = df_movies_copy['id'].astype('int64')\n",
        "# df_movies.head()\n",
        "\n",
        "df_movies_copy.rename(columns={'id': 'movieId'}, inplace=True)\n",
        "\n",
        "df_movies = df_movies_copy.copy()\n",
        "# Mengubah nama kolom dari 'id' menjadi 'movieId'\n",
        "#df_movies.rename(columns={'id': 'movieId'}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dd-XooPkK2gu"
      },
      "source": [
        "### Menangani Nilai Kosong (Missing Value)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada tap ini kita akan lakukan pengecekan nilai kosong pada variabel dataset df_movies dan ratings"
      ],
      "metadata": {
        "id": "0S2p6hOBIBaX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "HWNdOvNzK7l6",
        "outputId": "accddd5f-f0cf-4ad4-dd2d-ee52e1689364"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId         0\n",
              "genres          0\n",
              "title           3\n",
              "vote_average    3\n",
              "vote_count      3\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>genres</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vote_average</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vote_count</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df_movies.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeoKCc2mNRlY"
      },
      "source": [
        "Dari hasil diatas, nilai null terdapat pada variabel `title`, `vote_average` dan `vote_count` memiliki nilai null = 3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "1GgEBiX_N3eJ",
        "outputId": "e1a47718-7cec-4224-d675-d9f61a0cf67f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       movieId                                             genres title  \\\n",
              "19729    82663  [{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...   NaN   \n",
              "29502   122662  [{'id': 16, 'name': 'Animation'}, {'id': 878, ...   NaN   \n",
              "35586   249260  [{'id': 10770, 'name': 'TV Movie'}, {'id': 28,...   NaN   \n",
              "\n",
              "       vote_average  vote_count  \n",
              "19729           NaN         NaN  \n",
              "29502           NaN         NaN  \n",
              "35586           NaN         NaN  "
            ],
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              "      display:flex;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-54589e72-8267-45aa-8c78-65327867ea7a');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d4406baf-6d93-4a63-b246-ea5ed459d581 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Menampilkan baris yang memiliki nilai null\n",
        "df_movies[df_movies.isnull().any(axis = 1)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T24J1O2yE_Do"
      },
      "source": [
        "Dapat dilihat hasilnya terdapat beberap nilai kosong pada variabel dataset df_movies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "hrwS1GCaFOfI",
        "outputId": "ccd86755-05c2-42ce-c5d4-e1a1f2acadba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId         0\n",
              "genres          0\n",
              "title           0\n",
              "vote_average    0\n",
              "vote_count      0\n",
              "dtype: int64"
            ],
            "text/html": [
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              "      <th>vote_count</th>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df_movies = df_movies.dropna(subset=['title', 'vote_average', 'vote_count'])\n",
        "df_movies.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "bkntSp-KK_1o",
        "outputId": "a8e0330e-116a-4ea1-fa77-3723954bcd7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId       0\n",
              "movieId      0\n",
              "rating       0\n",
              "timestamp    0\n",
              "dtype: int64"
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              "\n",
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              "      <th>movieId</th>\n",
              "      <td>0</td>\n",
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          "metadata": {},
          "execution_count": 28
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      "source": [
        "ratings.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mFmZNx6WKofA"
      },
      "source": [
        "### Melihat Duplikat Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1Z42nArMu11"
      },
      "source": [
        "Langkah pertama kita cek variabel dataset df_movies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rl60V3APLGuC",
        "outputId": "1845b7f1-7908-4361-e170-5e55a8f69cfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df_movies.duplicated().sum()\n",
        "#df_movies.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TfJBMdNLwHI"
      },
      "source": [
        "Selanjutnya kita tampilkan movies yang duplikat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "Da-2H34wL1-0",
        "outputId": "3ba7369b-0bfd-410f-f27a-ec1beb3b1636"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       movieId                                             genres  \\\n",
              "1465    105045  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...   \n",
              "9165      5511  [{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...   \n",
              "9327     23305  [{'id': 12, 'name': 'Adventure'}, {'id': 16, '...   \n",
              "12066    14788  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...   \n",
              "13375   141971  [{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...   \n",
              "15074    22649  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...   \n",
              "15765    13209  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   \n",
              "16764   141971  [{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...   \n",
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              "20899   109962                      [{'id': 18, 'name': 'Drama'}]   \n",
              "21116    84198                [{'id': 99, 'name': 'Documentary'}]   \n",
              "21165   119916  [{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...   \n",
              "21854   152795  [{'id': 18, 'name': 'Drama'}, {'id': 878, 'nam...   \n",
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              "23044    25541                      [{'id': 18, 'name': 'Drama'}]   \n",
              "23534   110428                      [{'id': 18, 'name': 'Drama'}]   \n",
              "24844    11115  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
              "26625    69234  [{'id': 18, 'name': 'Drama'}, {'id': 27, 'name...   \n",
              "28860   168538  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   \n",
              "29374    42495  [{'id': 18, 'name': 'Drama'}, {'id': 10769, 'n...   \n",
              "30001   132641                      [{'id': 18, 'name': 'Drama'}]   \n",
              "33826     4912  [{'id': 35, 'name': 'Comedy'}, {'id': 80, 'nam...   \n",
              "35798   159849                [{'id': 99, 'name': 'Documentary'}]   \n",
              "38871    99080  [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...   \n",
              "40040   298721  [{'id': 18, 'name': 'Drama'}, {'id': 14, 'name...   \n",
              "40276    97995                     [{'id': 35, 'name': 'Comedy'}]   \n",
              "44826    12600  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
              "45265   265189  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
              "\n",
              "                                             title  vote_average  vote_count  \n",
              "1465                                   The Promise           5.0         1.0  \n",
              "9165                                   Le Samouraï           7.9       187.0  \n",
              "9327                                   The Warrior           6.3        15.0  \n",
              "12066                                       Bubble           6.4        36.0  \n",
              "13375                                     Blackout           6.7         3.0  \n",
              "15074                           A Farewell to Arms           6.2        29.0  \n",
              "15765                                      Offside           6.7        27.0  \n",
              "16764                                     Blackout           6.7         3.0  \n",
              "20843                                   Black Gold           5.9        77.0  \n",
              "20899                              Rich and Famous           4.9         7.0  \n",
              "21116                         A Place at the Table           6.9         7.0  \n",
              "21165                                  The Tempest           0.0         0.0  \n",
              "21854                                 The Congress           6.4       165.0  \n",
              "22151                             Days of Darkness           5.0         5.0  \n",
              "23044                                  Brotherhood           7.1        21.0  \n",
              "23534                         Camille Claudel 1915           7.0        20.0  \n",
              "24844                                         Deal           5.2        22.0  \n",
              "26625                     The Phantom of the Opera           5.0         3.0  \n",
              "28860               Nana, the True Key of Pleasure           4.7         3.0  \n",
              "29374                                    King Lear           8.0         3.0  \n",
              "30001                                         Wife           0.0         0.0  \n",
              "33826              Confessions of a Dangerous Mind           6.6       281.0  \n",
              "35798             Why We Fight: Divide and Conquer           5.0         1.0  \n",
              "38871                                   The Viking           0.0         0.0  \n",
              "40040                        Cemetery of Splendour           4.4        50.0  \n",
              "40276                         Seven Years Bad Luck           5.6         4.0  \n",
              "44826  Pokémon 4Ever: Celebi - Voice of the Forest           5.7        82.0  \n",
              "45265                                Force Majeure           6.8       255.0  "
            ],
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              "      <th>9165</th>\n",
              "      <td>5511</td>\n",
              "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...</td>\n",
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              "      <th>9327</th>\n",
              "      <td>23305</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 16, '...</td>\n",
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              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...</td>\n",
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              "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...</td>\n",
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              "      <td>22649</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...</td>\n",
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              "      <th>16764</th>\n",
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              "      <th>20899</th>\n",
              "      <td>109962</td>\n",
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              "      <th>21165</th>\n",
              "      <td>119916</td>\n",
              "      <td>[{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>21854</th>\n",
              "      <td>152795</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 878, 'nam...</td>\n",
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              "      <td>165.0</td>\n",
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              "      <th>22151</th>\n",
              "      <td>18440</td>\n",
              "      <td>[{'id': 28, 'name': 'Action'}, {'id': 27, 'nam...</td>\n",
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              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>23044</th>\n",
              "      <td>25541</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
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              "      <td>7.1</td>\n",
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              "    <tr>\n",
              "      <th>23534</th>\n",
              "      <td>110428</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
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              "      <th>24844</th>\n",
              "      <td>11115</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
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              "    <tr>\n",
              "      <th>26625</th>\n",
              "      <td>69234</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 27, 'name...</td>\n",
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              "      <th>28860</th>\n",
              "      <td>168538</td>\n",
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              "      <td>42495</td>\n",
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              "      <td>King Lear</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30001</th>\n",
              "      <td>132641</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
              "      <td>Wife</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33826</th>\n",
              "      <td>4912</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 80, 'nam...</td>\n",
              "      <td>Confessions of a Dangerous Mind</td>\n",
              "      <td>6.6</td>\n",
              "      <td>281.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35798</th>\n",
              "      <td>159849</td>\n",
              "      <td>[{'id': 99, 'name': 'Documentary'}]</td>\n",
              "      <td>Why We Fight: Divide and Conquer</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38871</th>\n",
              "      <td>99080</td>\n",
              "      <td>[{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...</td>\n",
              "      <td>The Viking</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40040</th>\n",
              "      <td>298721</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 14, 'name...</td>\n",
              "      <td>Cemetery of Splendour</td>\n",
              "      <td>4.4</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40276</th>\n",
              "      <td>97995</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>Seven Years Bad Luck</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44826</th>\n",
              "      <td>12600</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
              "      <td>Pokémon 4Ever: Celebi - Voice of the Forest</td>\n",
              "      <td>5.7</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45265</th>\n",
              "      <td>265189</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>Force Majeure</td>\n",
              "      <td>6.8</td>\n",
              "      <td>255.0</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_movies[df_movies\",\n  \"rows\": 28,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76458,\n        \"min\": 4912,\n        \"max\": 298721,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          109962,\n          25541,\n          84198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]\",\n          \"[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}]\",\n          \"[{'id': 18, 'name': 'Drama'}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Rich and Famous\",\n          \"Brotherhood\",\n          \"A Place at the Table\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1333426339082977,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          5.0,\n          5.6,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.15608958119859,\n        \"min\": 0.0,\n        \"max\": 281.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1.0,\n          4.0,\n          281.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Menampilkan movies duplikat\n",
        "df_movies[df_movies.duplicated]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUX8BhIOMR3X"
      },
      "source": [
        "Dari hasil di atas, terlihat bahwa ada data-data tersebut memang terduplikasi."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8cP3aIMqMUbP"
      },
      "outputs": [],
      "source": [
        "df_movies.drop_duplicates(inplace = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iqx1szuhNff7"
      },
      "source": [
        "Kedua kita cek dataset ratings yang duplikat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jzQ_-3pBLKoI",
        "outputId": "2a773f0e-4067-455d-b218-ddc00b7a3cfb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "ratings.duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9iPkeIY4NkwK"
      },
      "source": [
        "Hasil yang ditampilkan yakni 0 maka tidak ada duplikat data pada dataset ratings"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocesing\n",
        "Proses ini bertujuan untuk menyiapkan data mentah agar dapat digunakan secara efektif dalam model machine learning. Langkah-langkah yang dilakukan dalam proyak ini adalah sebagai berikut:\n",
        "* Mengurutkan pengguna dan film berdasarkan id\n",
        "* Mengubah fitur genres movie ke bentuk list\n",
        "* Melakukan penggabungan dataset ratings dan movies\n",
        "* Menghapus fitur yang tidak diperlukan\n",
        "* Mengambil Dataset sesuai kebutuhan"
      ],
      "metadata": {
        "id": "gxB2y5Prv_0S"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmP9EM36A7sr"
      },
      "source": [
        "### Mengurutkan pengguna berdasarkan ID"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1dWdm_L8BcHZ"
      },
      "outputs": [],
      "source": [
        "ratings   = ratings.sort_values('userId')\n",
        "df_movies = df_movies.sort_values('movieId')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Yt96uBAq_cA3",
        "outputId": "16bfa001-b77a-488b-c992-8cf64b988ca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    userId  movieId  rating   timestamp\n",
              "0        1       31     2.5  1260759144\n",
              "1        1     1029     3.0  1260759179\n",
              "12       1     1953     4.0  1260759191\n",
              "13       1     2105     4.0  1260759139\n",
              "14       1     2150     3.0  1260759194"
            ],
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-318546a5-f4ad-498d-b4e6-0dc82aaeb6d9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-318546a5-f4ad-498d-b4e6-0dc82aaeb6d9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f1df7d29-990f-4c20-afa5-5ff2aff2bc80\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f1df7d29-990f-4c20-afa5-5ff2aff2bc80')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f1df7d29-990f-4c20-afa5-5ff2aff2bc80 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ratings"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QodN2FsR_frM",
        "outputId": "9e4a5a1e-d553-4a56-bffd-b947e6a753ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       movieId                                             genres  \\\n",
              "4342         2  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...   \n",
              "12947        3  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   \n",
              "17           5  [{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...   \n",
              "474          6  [{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...   \n",
              "256         11  [{'id': 12, 'name': 'Adventure'}, {'id': 28, '...   \n",
              "\n",
              "                     title  vote_average  vote_count  \n",
              "4342                 Ariel           7.1        44.0  \n",
              "12947  Shadows in Paradise           7.1        35.0  \n",
              "17              Four Rooms           6.5       539.0  \n",
              "474         Judgment Night           6.4        79.0  \n",
              "256              Star Wars           8.1      6778.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2987bd6-f32b-4ca0-a750-f3cba47182ee\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>genres</th>\n",
              "      <th>title</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
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              "      <th>4342</th>\n",
              "      <td>2</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...</td>\n",
              "      <td>Ariel</td>\n",
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              "      <td>44.0</td>\n",
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              "    <tr>\n",
              "      <th>12947</th>\n",
              "      <td>3</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
              "      <td>Shadows in Paradise</td>\n",
              "      <td>7.1</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5</td>\n",
              "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...</td>\n",
              "      <td>Four Rooms</td>\n",
              "      <td>6.5</td>\n",
              "      <td>539.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>6</td>\n",
              "      <td>[{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...</td>\n",
              "      <td>Judgment Night</td>\n",
              "      <td>6.4</td>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>11</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 28, '...</td>\n",
              "      <td>Star Wars</td>\n",
              "      <td>8.1</td>\n",
              "      <td>6778.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c2987bd6-f32b-4ca0-a750-f3cba47182ee button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c2987bd6-f32b-4ca0-a750-f3cba47182ee');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5a3ee877-7b2a-42df-8eab-1d9fbc827936\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a3ee877-7b2a-42df-8eab-1d9fbc827936')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5a3ee877-7b2a-42df-8eab-1d9fbc827936 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_movies",
              "summary": "{\n  \"name\": \"df_movies\",\n  \"rows\": 45432,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112480,\n        \"min\": 2,\n        \"max\": 469172,\n        \"num_unique_values\": 45430,\n        \"samples\": [\n          43017,\n          57409,\n          31147\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4065,\n        \"samples\": [\n          \"[{'id': 10770, 'name': 'TV Movie'}, {'id': 27, 'name': 'Horror'}, {'id': 53, 'name': 'Thriller'}]\",\n          \"[{'id': 10751, 'name': 'Family'}, {'id': 37, 'name': 'Western'}]\",\n          \"[{'id': 9648, 'name': 'Mystery'}, {'id': 28, 'name': 'Action'}, {'id': 35, 'name': 'Comedy'}, {'id': 80, 'name': 'Crime'}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 42277,\n        \"samples\": [\n          \"Altered\",\n          \"The New Rulers of the World\",\n          \"Margaret Thatcher: The Long Walk to Finchley\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9241005782931258,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          4.4,\n          5.7,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 491.45555175027505,\n        \"min\": 0.0,\n        \"max\": 14075.0,\n        \"num_unique_values\": 1820,\n        \"samples\": [\n          1618.0,\n          709.0,\n          83.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9oIaeUDx8vTF"
      },
      "source": [
        "### Mengubah fitur genres movie ke bentuk list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sFGYnh2N86Nz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "1cc9974f-b5af-4c58-ad62-90d2bada8fe6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4342                           [Drama, Crime]\n",
              "12947                         [Drama, Comedy]\n",
              "17                            [Crime, Comedy]\n",
              "474                 [Action, Thriller, Crime]\n",
              "256      [Adventure, Action, Science Fiction]\n",
              "                         ...                 \n",
              "45078                        [Fantasy, Drama]\n",
              "45273                                 [Drama]\n",
              "21891                        [Drama, Romance]\n",
              "45398                       [Romance, Comedy]\n",
              "20189                        [Fantasy, Drama]\n",
              "Name: genres, Length: 45432, dtype: object"
            ],
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              "      <th>474</th>\n",
              "      <td>[Action, Thriller, Crime]</td>\n",
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              "      <th>256</th>\n",
              "      <td>[Adventure, Action, Science Fiction]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>45078</th>\n",
              "      <td>[Fantasy, Drama]</td>\n",
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              "      <td>[Drama]</td>\n",
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              "      <th>21891</th>\n",
              "      <td>[Drama, Romance]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45398</th>\n",
              "      <td>[Romance, Comedy]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20189</th>\n",
              "      <td>[Fantasy, Drama]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45432 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df_movies['genres'] = df_movies['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\n",
        "df_movies['genres']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXmMTIWTDYiq"
      },
      "source": [
        "### Melakukan penggabungan Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cN1jGFvSUQRj"
      },
      "source": [
        "Pada tahap ini kita akan mengabungkan dataset ratings dan dataset movies menggunakan fungsi `merge` dan mengahapus variabel `timestamp` yang dibutuhkan."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nokA6AEBDkLi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "bca07dd2-3a98-454a-9b34-f273c70be57c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   userId  movieId  rating   timestamp                    genres  \\\n",
              "0       1     2105     4.0  1260759139         [Comedy, Romance]   \n",
              "1       1     2193     2.0  1260759198  [Comedy, Drama, Romance]   \n",
              "2       1     2294     2.0  1260759108                  [Comedy]   \n",
              "3       1     2455     2.5  1260759113    [Drama, Comedy, Crime]   \n",
              "4       1     1371     2.5  1260759135                   [Drama]   \n",
              "\n",
              "                            title  vote_average  vote_count  \n",
              "0                    American Pie           6.4      2358.0  \n",
              "1                        My Tutor           5.8        17.0  \n",
              "2  Jay and Silent Bob Strike Back           6.4       491.0  \n",
              "3            Confidentially Yours           7.1        25.0  \n",
              "4                       Rocky III           6.6       894.0  "
            ],
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              "type": "dataframe",
              "variable_name": "df_movies_ratings",
              "summary": "{\n  \"name\": \"df_movies_ratings\",\n  \"rows\": 44989,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 194,\n        \"min\": 1,\n        \"max\": 671,\n        \"num_unique_values\": 671,\n        \"samples\": [\n          362,\n          159,\n          481\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15919,\n        \"min\": 2,\n        \"max\": 160718,\n        \"num_unique_values\": 2830,\n        \"samples\": [\n          1686,\n          26700,\n          50189\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0532009078403732,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.5,\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 187873874,\n        \"min\": 789652009,\n        \"max\": 1476623282,\n        \"num_unique_values\": 36080,\n        \"samples\": [\n          1463337247,\n          1304142199,\n          879507166\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2794,\n        \"samples\": [\n          \"The Weeping Camel\",\n          \"Mildred Pierce\",\n          \"Willy Wonka & the Chocolate Factory\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0595966563825518,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          7.5,\n          2.5,\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1219.4634779328358,\n        \"min\": 0.0,\n        \"max\": 12269.0,\n        \"num_unique_values\": 887,\n        \"samples\": [\n          123.0,\n          1616.0,\n          305.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df_movies_ratings = pd.merge(ratings, df_movies, on='movieId', how='inner')\n",
        "df_movies_ratings.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Menghapus fitur yang tidak diperlukan"
      ],
      "metadata": {
        "id": "98CPhA_dIjVu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selanjutnya, menghapus fitur-firur yang tidak diperlukan yaitu `timestamp`, `vote_average` dan `vote_count`."
      ],
      "metadata": {
        "id": "OAReDVVRIxie"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies_ratings.drop(['timestamp', 'vote_average', 'vote_count'],axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "YQ_BI8MuIqMm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selanjutnya gunakan fungsi `head` untuk melihat hasilnya."
      ],
      "metadata": {
        "id": "h04zcmjTJH4z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies_ratings.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "JTTOvRS2JONg",
        "outputId": "41df9f73-fa50-4b6c-846c-bbb56a0600d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId  rating                                        genres  \\\n",
              "0       1     2105     4.0                             [Comedy, Romance]   \n",
              "1       1     2193     2.0                      [Comedy, Drama, Romance]   \n",
              "2       1     2294     2.0                                      [Comedy]   \n",
              "3       1     2455     2.5                        [Drama, Comedy, Crime]   \n",
              "4       1     1371     2.5                                       [Drama]   \n",
              "5       1     1405     1.0                              [Drama, History]   \n",
              "6       2      168     3.0                  [Science Fiction, Adventure]   \n",
              "7       2      165     3.0  [Adventure, Comedy, Family, Science Fiction]   \n",
              "8       2      161     3.0                             [Thriller, Crime]   \n",
              "9       2      153     4.0                                       [Drama]   \n",
              "\n",
              "                            title  \n",
              "0                    American Pie  \n",
              "1                        My Tutor  \n",
              "2  Jay and Silent Bob Strike Back  \n",
              "3            Confidentially Yours  \n",
              "4                       Rocky III  \n",
              "5                           Greed  \n",
              "6   Star Trek IV: The Voyage Home  \n",
              "7      Back to the Future Part II  \n",
              "8                  Ocean's Eleven  \n",
              "9             Lost in Translation  "
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_movies_ratings",
              "summary": "{\n  \"name\": \"df_movies_ratings\",\n  \"rows\": 44989,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 194,\n        \"min\": 1,\n        \"max\": 671,\n        \"num_unique_values\": 671,\n        \"samples\": [\n          362,\n          159,\n          481\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15919,\n        \"min\": 2,\n        \"max\": 160718,\n        \"num_unique_values\": 2830,\n        \"samples\": [\n          1686,\n          26700,\n          50189\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0532009078403732,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.5,\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2794,\n        \"samples\": [\n          \"The Weeping Camel\",\n          \"Mildred Pierce\",\n          \"Willy Wonka & the Chocolate Factory\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJXVFbQSsbTR"
      },
      "source": [
        "### Mengambil Dataset sesuai kebutuhan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLdzDAiesphQ"
      },
      "source": [
        "Pada tahap ini, kita mengambil 20000 gabugan dataset movies dan ratings dengan teknik sampling menggunakan fungsi `shuffle` dari library `sklearn.utils` untuk mengambil data secara acak dataset besar untuk mempermudah pengolahan dan mencegah **crash**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9EJg8ELXtBjg",
        "outputId": "63d7b8d9-1048-44f7-d831-0fe1b2d04eae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       userId  movieId  rating                                  genres  \\\n",
              "16328     258     3479     3.0                       [Thriller, Crime]   \n",
              "240         5      586     4.0                         [Comedy, Drama]   \n",
              "13251     212     8371     4.0                                 [Drama]   \n",
              "30595     472      920     4.5  [Animation, Adventure, Comedy, Family]   \n",
              "17173     275     4995     4.5                                 [Drama]   \n",
              "...       ...      ...     ...                                     ...   \n",
              "5400       78     4369     4.5                        [Drama, Romance]   \n",
              "28375     452     2094     2.5                 [Drama, Music, Romance]   \n",
              "33435     518      377     4.0                                [Horror]   \n",
              "16698     265     3089     4.0                               [Western]   \n",
              "24308     388     5425     2.0                                [Horror]   \n",
              "\n",
              "                           title  \n",
              "16328                Dirty Hands  \n",
              "240                  Wag the Dog  \n",
              "13251                Guter Junge  \n",
              "30595                       Cars  \n",
              "17173              Boogie Nights  \n",
              "...                          ...  \n",
              "5400     Just a Question of Love  \n",
              "28375                 Roustabout  \n",
              "33435  A Nightmare on Elm Street  \n",
              "16698                  Red River  \n",
              "24308        Beyond the Darkness  \n",
              "\n",
              "[20000 rows x 5 columns]"
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5400</th>\n",
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              "      <th>33435</th>\n",
              "      <td>518</td>\n",
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              "      <td>[Horror]</td>\n",
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              "      <th>16698</th>\n",
              "      <td>265</td>\n",
              "      <td>3089</td>\n",
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              "      <td>Red River</td>\n",
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              "    <tr>\n",
              "      <th>24308</th>\n",
              "      <td>388</td>\n",
              "      <td>5425</td>\n",
              "      <td>2.0</td>\n",
              "      <td>[Horror]</td>\n",
              "      <td>Beyond the Darkness</td>\n",
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              "<p>20000 rows × 5 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_sample_final",
              "summary": "{\n  \"name\": \"df_sample_final\",\n  \"rows\": 20000,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195,\n        \"min\": 1,\n        \"max\": 671,\n        \"num_unique_values\": 671,\n        \"samples\": [\n          107,\n          624,\n          525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15977,\n        \"min\": 2,\n        \"max\": 160718,\n        \"num_unique_values\": 2249,\n        \"samples\": [\n          48791,\n          249,\n          61361\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.053410856739585,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.5,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2221,\n        \"samples\": [\n          \"Dawn of the Dead\",\n          \"Premium Rush\",\n          \"Cruel Intentions 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df_sample_final = shuffle(df_movies_ratings).head(20000)\n",
        "df_sample_final"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mAr4b66ZBx8P"
      },
      "source": [
        "# Content Based Filtering (CBF)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Content-Based Filtering adalah metode dalam sistem rekomendasi yang memberikan rekomendasi berdasarkan karakteristik atau konten dari item yang telah disukai atau dinilai oleh pengguna."
      ],
      "metadata": {
        "id": "uz1Af4sp3R2d"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G25EiOTmJ_te"
      },
      "source": [
        "## A. Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Teknik yang digunakan yaitu teknik TF-IDF (Term Frequency-Inverse Document Frequency) untuk menentukan bobot fitur dan menghitung kesamaan antara item. Pada proyek ini item yang aka kita gunaka yaitu `genres` film."
      ],
      "metadata": {
        "id": "mfZ6DXEiRRJd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Langkah yang pertama kita cek datset kita sesuai dengan kebutuhan yang kita inginkan dengan fungsi `info()`"
      ],
      "metadata": {
        "id": "WISVmDLq3r1G"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gimndLyRa_kc",
        "outputId": "abdb6a67-6495-4420-9453-081a10c14b30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 20000 entries, 16328 to 24308\n",
            "Data columns (total 5 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   userId   20000 non-null  int64  \n",
            " 1   movieId  20000 non-null  int64  \n",
            " 2   rating   20000 non-null  float64\n",
            " 3   genres   20000 non-null  object \n",
            " 4   title    20000 non-null  object \n",
            "dtypes: float64(1), int64(2), object(2)\n",
            "memory usage: 937.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df_sample_final.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapa dilihat hasil informasi dataset kita terdiri dari 20000 baris dan 5 kolom dengan 1 tipe data float64, 2 tipe data int64 dan 2 tipe data object."
      ],
      "metadata": {
        "id": "K3HCuiHk342C"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCy9wMU31_vD"
      },
      "source": [
        "Berikutnya, kita bisa melanjutkan ke tahap persiapan dengan membuat variabel preparation yang berisi dataframe df_sample_final kemudian mengurutkan berdasarkan `movieId`"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "g-55tWDd2AkC",
        "outputId": "7462ccce-23f8-46b2-d360-0a7a0e5f8fe0"
      },
      "outputs": [
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          "data": {
            "text/plain": [
              "       userId  movieId  rating                                 genres  \\\n",
              "4350       69        2     3.5                         [Drama, Crime]   \n",
              "11268     177        2     5.0                         [Drama, Crime]   \n",
              "4647       73        2     2.5                         [Drama, Crime]   \n",
              "2687       32        2     4.0                         [Drama, Crime]   \n",
              "20364     313        2     3.0                         [Drama, Crime]   \n",
              "...       ...      ...     ...                                    ...   \n",
              "613        15   140174     4.0             [Action, Horror, Thriller]   \n",
              "31395     486   140174     3.5             [Action, Horror, Thriller]   \n",
              "25282     402   148652     2.5                             [Thriller]   \n",
              "586        15   158238     3.5                                     []   \n",
              "35741     547   160718     4.0  [TV Movie, Action, Adventure, Comedy]   \n",
              "\n",
              "                     title  \n",
              "4350                 Ariel  \n",
              "11268                Ariel  \n",
              "4647                 Ariel  \n",
              "2687                 Ariel  \n",
              "20364                Ariel  \n",
              "...                    ...  \n",
              "613    Rise of the Zombies  \n",
              "31395  Rise of the Zombies  \n",
              "25282  The Eleventh Victim  \n",
              "586            Stolen Seas  \n",
              "35741        I Spy Returns  \n",
              "\n",
              "[20000 rows x 5 columns]"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"preparation\",\n  \"rows\": 20000,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195,\n        \"min\": 1,\n        \"max\": 671,\n        \"num_unique_values\": 671,\n        \"samples\": [\n          4,\n          171,\n          395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15977,\n        \"min\": 2,\n        \"max\": 160718,\n        \"num_unique_values\": 2249,\n        \"samples\": [\n          104374,\n          840,\n          26326\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0534108567395852,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.0,\n          5.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2221,\n        \"samples\": [\n          \"Breakfast at Tiffany's\",\n          \"The Baby of M\\u00e2con\",\n          \"Big Bad Mama\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "preparation = df_sample_final\n",
        "preparation.sort_values('movieId')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "_NSQ1Ov-2exx",
        "outputId": "05f61360-1d84-4373-c571-242159f8c3da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       userId  movieId  rating                                  genres  \\\n",
              "16328     258     3479     3.0                       [Thriller, Crime]   \n",
              "240         5      586     4.0                         [Comedy, Drama]   \n",
              "13251     212     8371     4.0                                 [Drama]   \n",
              "30595     472      920     4.5  [Animation, Adventure, Comedy, Family]   \n",
              "17173     275     4995     4.5                                 [Drama]   \n",
              "...       ...      ...     ...                                     ...   \n",
              "37482     564     3574     1.0                      [Thriller, Horror]   \n",
              "37481     564     3576     5.0                       [Drama, Thriller]   \n",
              "35579     547    26467     1.0                                [Action]   \n",
              "10172     157    80350     0.5               [Thriller, Action, Drama]   \n",
              "24308     388     5425     2.0                                [Horror]   \n",
              "\n",
              "                       title  \n",
              "16328            Dirty Hands  \n",
              "240              Wag the Dog  \n",
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              "30595                   Cars  \n",
              "17173          Boogie Nights  \n",
              "...                      ...  \n",
              "37482               Doctor X  \n",
              "37481  Calling Dr. Gillespie  \n",
              "35579              The Climb  \n",
              "10172       Tough and Deadly  \n",
              "24308    Beyond the Darkness  \n",
              "\n",
              "[2249 rows x 5 columns]"
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              "      <td>[Animation, Adventure, Comedy, Family]</td>\n",
              "      <td>Cars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17173</th>\n",
              "      <td>275</td>\n",
              "      <td>4995</td>\n",
              "      <td>4.5</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Boogie Nights</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37482</th>\n",
              "      <td>564</td>\n",
              "      <td>3574</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[Thriller, Horror]</td>\n",
              "      <td>Doctor X</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37481</th>\n",
              "      <td>564</td>\n",
              "      <td>3576</td>\n",
              "      <td>5.0</td>\n",
              "      <td>[Drama, Thriller]</td>\n",
              "      <td>Calling Dr. Gillespie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35579</th>\n",
              "      <td>547</td>\n",
              "      <td>26467</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[Action]</td>\n",
              "      <td>The Climb</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10172</th>\n",
              "      <td>157</td>\n",
              "      <td>80350</td>\n",
              "      <td>0.5</td>\n",
              "      <td>[Thriller, Action, Drama]</td>\n",
              "      <td>Tough and Deadly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24308</th>\n",
              "      <td>388</td>\n",
              "      <td>5425</td>\n",
              "      <td>2.0</td>\n",
              "      <td>[Horror]</td>\n",
              "      <td>Beyond the Darkness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2249 rows × 5 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "  <div id=\"id_e5a27659-961a-4159-abaa-b6526bcdd782\">\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
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              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "preparation",
              "summary": "{\n  \"name\": \"preparation\",\n  \"rows\": 2249,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 194,\n        \"min\": 2,\n        \"max\": 669,\n        \"num_unique_values\": 429,\n        \"samples\": [\n          71,\n          14,\n          555\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26513,\n        \"min\": 2,\n        \"max\": 160718,\n        \"num_unique_values\": 2249,\n        \"samples\": [\n          48791,\n          249,\n          61361\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1269851493420673,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.5,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2221,\n        \"samples\": [\n          \"Dawn of the Dead\",\n          \"Premium Rush\",\n          \"Cruel Intentions 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# Membuang data duplikat pada variabel preparation\n",
        "preparation = preparation.drop_duplicates('movieId')\n",
        "preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6JBUIVI26NY"
      },
      "source": [
        "Selanjutnya, kita perlu melakukan konversi data series menjadi list. Dalam hal ini, kita menggunakan fungsi `tolist()` dari library numpy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69U2pkPc26lh",
        "outputId": "40910128-7b21-4d6c-de28-061dc73fa24a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2249\n",
            "2249\n",
            "2249\n"
          ]
        }
      ],
      "source": [
        "# Mengonversi data series 'movieId' menjadi dalam bentuk list\n",
        "movie_id = preparation['movieId'].tolist()\n",
        "\n",
        "# Mengonversi data series ‘Title’ menjadi dalam bentuk list\n",
        "movie_name = preparation['title'].tolist()\n",
        "\n",
        "# Mengonversi data series ‘Genres’ menjadi dalam bentuk list\n",
        "movie_genres = preparation['genres'].tolist()\n",
        "\n",
        "print(len(movie_id))\n",
        "print(len(movie_name))\n",
        "print(len(movie_genres))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6YkmA-U1hEP"
      },
      "source": [
        "Tahap berikutnya, kita akan membuat dictionary untuk menentukan pasangan key-value pada data `movie_id`, `movie_name` dan `movie_genres` yang telah kita siapkan sebelumnya."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-yCygExt3kt-",
        "outputId": "305c12e6-e6a0-4e5a-8ea1-3586b36d1f12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id             movie_name                                  genres\n",
              "0      3479            Dirty Hands                       [Thriller, Crime]\n",
              "1       586            Wag the Dog                         [Comedy, Drama]\n",
              "2      8371            Guter Junge                                 [Drama]\n",
              "3       920                   Cars  [Animation, Adventure, Comedy, Family]\n",
              "4      4995          Boogie Nights                                 [Drama]\n",
              "...     ...                    ...                                     ...\n",
              "2244   3574               Doctor X                      [Thriller, Horror]\n",
              "2245   3576  Calling Dr. Gillespie                       [Drama, Thriller]\n",
              "2246  26467              The Climb                                [Action]\n",
              "2247  80350       Tough and Deadly               [Thriller, Action, Drama]\n",
              "2248   5425    Beyond the Darkness                                [Horror]\n",
              "\n",
              "[2249 rows x 3 columns]"
            ],
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              "      <td>3576</td>\n",
              "      <td>Calling Dr. Gillespie</td>\n",
              "      <td>[Drama, Thriller]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2246</th>\n",
              "      <td>26467</td>\n",
              "      <td>The Climb</td>\n",
              "      <td>[Action]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2247</th>\n",
              "      <td>80350</td>\n",
              "      <td>Tough and Deadly</td>\n",
              "      <td>[Thriller, Action, Drama]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2248</th>\n",
              "      <td>5425</td>\n",
              "      <td>Beyond the Darkness</td>\n",
              "      <td>[Horror]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2249 rows × 3 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movies_new",
              "summary": "{\n  \"name\": \"movies_new\",\n  \"rows\": 2249,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26513,\n        \"min\": 2,\n        \"max\": 160718,\n        \"num_unique_values\": 2249,\n        \"samples\": [\n          48791,\n          249,\n          61361\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2221,\n        \"samples\": [\n          \"Dawn of the Dead\",\n          \"Premium Rush\",\n          \"Cruel Intentions 2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Membuat dictionary untuk data ‘movie_id’, ‘movie_name’, dan genres\n",
        "movies_new = pd.DataFrame({\n",
        "    'id': movie_id,\n",
        "    'movie_name': movie_name,\n",
        "    'genres': movie_genres\n",
        "})\n",
        "movies_new"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selanjutnya langkah berikut kita gunakan fungsi `TfidfVectorizer` untuk mengkonversi `genres`. Namusn sebelum itu `genres` perlu kita konversi dari list ke siting akar dapay diproses."
      ],
      "metadata": {
        "id": "Te29NwY54III"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = movies_new\n",
        "# Mengonversi list menjadi string dengan pengecekan tipe data\n",
        "data['genres'] = data['genres'].apply(lambda x: ', '.join(x) if isinstance(x, list) else '')"
      ],
      "metadata": {
        "id": "gxNm1A5lgEbL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8G6W5kTfByFF",
        "outputId": "991b8990-09fd-45ed-bb88-78e5f48b7587"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['action', 'adventure', 'animation', 'comedy', 'crime',\n",
              "       'documentary', 'drama', 'family', 'fantasy', 'fiction', 'foreign',\n",
              "       'history', 'horror', 'movie', 'music', 'mystery', 'romance',\n",
              "       'science', 'thriller', 'tv', 'war', 'western'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "# Inisialisasi TfidfVectorizer\n",
        "tf_cbf = TfidfVectorizer()\n",
        "\n",
        "# Melakukan perhitungan idf pada data track_genre\n",
        "tf_cbf.fit(data[\"genres\"])\n",
        "\n",
        "# Mapping array dari fitur index integer ke fitur nama\n",
        "tf_cbf.get_feature_names_out()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah mendapat index seluruh genre film, akan difit lalu ditransformasikan ke bentuk matriks."
      ],
      "metadata": {
        "id": "U_vAZlVjafH2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Melakukan fit lalu ditransformasikan ke bentuk matrix\n",
        "tfidf_matrix = tf_cbf.fit_transform(data[\"genres\"])\n",
        "\n",
        "# Melihat ukuran matrix tfidf\n",
        "tfidf_matrix.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umRHGwUBafpf",
        "outputId": "9bf12524-ddf6-4761-b459-115abea65ec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2249, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah dilakukan fit dan ditransformasikan ke bentuk matrix diperoleh ukuran (2266 x 22). Selanjutnya kita akan mengubah vektor tf-idf dalam bentuk matriks dengan fungsi `todense()`."
      ],
      "metadata": {
        "id": "ZRJf5EV6Y7xO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengubah vektor tf-idf dalam bentuk matriks dengan fungsi todense()\n",
        "tfidf_matrix.todense()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBb5eMCUd6d7",
        "outputId": "a8d3e49d-fd64-4af7-d824-c0a99b37b66d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "matrix([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "         0.        ],\n",
              "        [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "         0.        ],\n",
              "        [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "         0.        ],\n",
              "        ...,\n",
              "        [1.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "         0.        ],\n",
              "        [0.67327774, 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "         0.        ],\n",
              "        [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "         0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah dibentuk matriks, dibuat tabel berisi judul film beserta genrenya berdasarkan TF-IDF yang telah diinisiasi."
      ],
      "metadata": {
        "id": "IGlA-0f04xJ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Membentuk tabel dari judul film beserta genrenya berdasarkan tfidf\n",
        "pd.DataFrame(\n",
        "    tfidf_matrix.todense(),\n",
        "    columns = tf_cbf.get_feature_names_out(),\n",
        "    index = data.movie_name\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "WxVHvmi8ef4k",
        "outputId": "185f2012-f656-4280-bc6d-03eb3c93bf5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         action  adventure  animation    comedy     crime  \\\n",
              "movie_name                                                                  \n",
              "Dirty Hands            0.000000   0.000000   0.000000  0.000000  0.766458   \n",
              "Wag the Dog            0.000000   0.000000   0.000000  0.810865  0.000000   \n",
              "Guter Junge            0.000000   0.000000   0.000000  0.000000  0.000000   \n",
              "Cars                   0.000000   0.424724   0.647281  0.310091  0.000000   \n",
              "Boogie Nights          0.000000   0.000000   0.000000  0.000000  0.000000   \n",
              "...                         ...        ...        ...       ...       ...   \n",
              "Doctor X               0.000000   0.000000   0.000000  0.000000  0.000000   \n",
              "Calling Dr. Gillespie  0.000000   0.000000   0.000000  0.000000  0.000000   \n",
              "The Climb              1.000000   0.000000   0.000000  0.000000  0.000000   \n",
              "Tough and Deadly       0.673278   0.000000   0.000000  0.000000  0.000000   \n",
              "Beyond the Darkness    0.000000   0.000000   0.000000  0.000000  0.000000   \n",
              "\n",
              "                       documentary     drama    family  fantasy  fiction  ...  \\\n",
              "movie_name                                                                ...   \n",
              "Dirty Hands                    0.0  0.000000  0.000000      0.0      0.0  ...   \n",
              "Wag the Dog                    0.0  0.585233  0.000000      0.0      0.0  ...   \n",
              "Guter Junge                    0.0  1.000000  0.000000      0.0      0.0  ...   \n",
              "Cars                           0.0  0.000000  0.551798      0.0      0.0  ...   \n",
              "Boogie Nights                  0.0  1.000000  0.000000      0.0      0.0  ...   \n",
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              "movie_name                                                                \n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2VHcpR4NdHw"
      },
      "source": [
        "## B. Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada tahp ini kita gunakan metode `Consine Similarity`,  yang berfungsi mengukur kesamaan antara dua dokumen atau vektor dalam ruang multidimensi. Pada proyek ini, kita akan gunakan untuk sistem rekomendasi berbasis `Content-Based Filtering` yang memberikan rekomendasi berdasarkan karakteristik atau konten dari item genre film yang telah disukai atau dinilai oleh pengguna. Menurut Firmansyah(2018), `Cosine similarity` digunakan dalam ruang positif, dimana hasilnya dibatasi antara nilai `0` dan `1`. Kalau nilainya `0` maka dokumen tersebut dikatakan mirip jika hasilnya 1 maka nilai tersebut dikatakan tidak mirip Perhatikan bahwa batas ini berlaku untuk sejumlah dimensi."
      ],
      "metadata": {
        "id": "-78_07745uFN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Untuk menentukan content-based filtering, pada proyek ini digunakan cosine similarity untuk mencari kemiripan antar film."
      ],
      "metadata": {
        "id": "FOczkkPvYjXy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung cosine similarity pada matrix tf-idf\n",
        "cosine_sim = cosine_similarity(tfidf_matrix)\n",
        "cosine_sim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Px6Yr55tYnDE",
        "outputId": "ac6cc9b1-1312-486b-9a70-4cfe3a15ce19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.        , 0.        , ..., 0.        , 0.39513814,\n",
              "        0.        ],\n",
              "       [0.        , 1.        , 0.58523313, ..., 0.        , 0.24003718,\n",
              "        0.        ],\n",
              "       [0.        , 0.58523313, 1.        , ..., 0.        , 0.41015651,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 1.        , 0.67327774,\n",
              "        0.        ],\n",
              "       [0.39513814, 0.24003718, 0.41015651, ..., 0.67327774, 1.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung cosine similarity\n",
        "cosine_sim_df = pd.DataFrame(cosine_sim, index = data[\"movie_name\"], columns = data[\"movie_name\"])\n",
        "print('Shape:', cosine_sim_df.shape)\n",
        "\n",
        "# Melihat similarity matrix pada setiap film\n",
        "cosine_sim_df.sample(10, axis = 1).sample(10, axis = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "V82AE-3ZYSRk",
        "outputId": "168d7fb8-dc44-45c9-e7d3-1086e9f3f87e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (2249, 2249)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movie_name                 Annie's Point       xXx  Bollywood/Hollywood  \\\n",
              "movie_name                                                                \n",
              "The Very Thought of You         0.000000  0.000000             0.598838   \n",
              "28 Weeks Later                  0.000000  0.204161             0.000000   \n",
              "The Ox-Bow Incident             0.105607  0.258993             0.078291   \n",
              "The Time Machine                0.000000  0.575908             0.000000   \n",
              "Shaft                           0.000000  0.852840             0.000000   \n",
              "Oldboy                          0.113519  0.510835             0.084156   \n",
              "Désirée                         0.195417  0.000000             0.535912   \n",
              "The War of the Gargantuas       0.000000  0.240790             0.000000   \n",
              "The Rink                        0.000000  0.000000             0.386062   \n",
              "The Pledge                      0.110972  0.227222             0.082268   \n",
              "\n",
              "movie_name                 Maid in Manhattan  Forrest Gump  Paranoid Park  \\\n",
              "movie_name                                                                  \n",
              "The Very Thought of You             0.906659      0.906659       0.000000   \n",
              "28 Weeks Later                      0.000000      0.000000       0.000000   \n",
              "The Ox-Bow Incident                 0.118534      0.118534       0.092527   \n",
              "The Time Machine                    0.000000      0.000000       0.000000   \n",
              "Shaft                               0.000000      0.000000       0.307773   \n",
              "Oldboy                              0.127415      0.127415       0.598545   \n",
              "Désirée                             0.811387      0.811387       0.171215   \n",
              "The War of the Gargantuas           0.000000      0.000000       0.000000   \n",
              "The Rink                            0.584510      0.584510       0.000000   \n",
              "The Pledge                          0.124556      0.124556       0.896594   \n",
              "\n",
              "movie_name                    Helen  Backdraft  The Murderers Are Among Us  \\\n",
              "movie_name                                                                   \n",
              "The Very Thought of You    0.000000   0.000000                    0.000000   \n",
              "28 Weeks Later             0.000000   0.158572                    0.000000   \n",
              "The Ox-Bow Incident        0.280978   0.275813                    0.280978   \n",
              "The Time Machine           0.000000   0.191439                    0.000000   \n",
              "Shaft                      0.000000   0.631565                    0.000000   \n",
              "Oldboy                     0.302030   0.879687                    0.302030   \n",
              "Désirée                    0.519929   0.138141                    0.519929   \n",
              "The War of the Gargantuas  0.000000   0.187021                    0.000000   \n",
              "The Rink                   0.000000   0.000000                    0.000000   \n",
              "The Pledge                 0.295253   0.899880                    0.295253   \n",
              "\n",
              "movie_name                 The Thomas Crown Affair  \n",
              "movie_name                                          \n",
              "The Very Thought of You                   0.478029  \n",
              "28 Weeks Later                            0.000000  \n",
              "The Ox-Bow Incident                       0.106944  \n",
              "The Time Machine                          0.000000  \n",
              "Shaft                                     0.355729  \n",
              "Oldboy                                    0.114957  \n",
              "Désirée                                   0.732052  \n",
              "The War of the Gargantuas                 0.000000  \n",
              "The Rink                                  0.000000  \n",
              "The Pledge                                0.472392  "
            ],
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              "\n",
              "  <div id=\"df-68dbb0e3-0ec6-48ce-b665-65e090134de8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th>movie_name</th>\n",
              "      <th>Annie's Point</th>\n",
              "      <th>xXx</th>\n",
              "      <th>Bollywood/Hollywood</th>\n",
              "      <th>Maid in Manhattan</th>\n",
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              "      <th>Paranoid Park</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.105607</td>\n",
              "      <td>0.258993</td>\n",
              "      <td>0.078291</td>\n",
              "      <td>0.118534</td>\n",
              "      <td>0.118534</td>\n",
              "      <td>0.092527</td>\n",
              "      <td>0.280978</td>\n",
              "      <td>0.275813</td>\n",
              "      <td>0.280978</td>\n",
              "      <td>0.106944</td>\n",
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              "      <th>The Time Machine</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.575908</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.852840</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.307773</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.631565</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.355729</td>\n",
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              "      <th>Oldboy</th>\n",
              "      <td>0.113519</td>\n",
              "      <td>0.510835</td>\n",
              "      <td>0.084156</td>\n",
              "      <td>0.127415</td>\n",
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              "      <td>0.302030</td>\n",
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              "      <td>0.302030</td>\n",
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              "      <th>Désirée</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>The Pledge</th>\n",
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              "      <td>0.082268</td>\n",
              "      <td>0.124556</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"cosine_sim_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"movie_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"The Rink\",\n          \"28 Weeks Later\",\n          \"Oldboy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annie's Point\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07220754564161329,\n        \"min\": 0.0,\n        \"max\": 0.19541714641508817,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.10560663609360392,\n          0.11097173908783149,\n          0.11351904208756328\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"xXx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2817026779857316,\n        \"min\": 0.0,\n        \"max\": 0.8528404351288669,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.20416114982643016,\n          0.5108347445847131,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bollywood/Hollywood\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.236437437208514,\n        \"min\": 0.0,\n        \"max\": 0.5988383944386525,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.5988383944386525,\n          0.0,\n          0.3860620243240569\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Maid in Manhattan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.357973404406404,\n        \"min\": 0.0,\n        \"max\": 0.9066593737328417,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9066593737328417,\n          0.0,\n          0.5845095378758998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Forrest Gump\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.357973404406404,\n        \"min\": 0.0,\n        \"max\": 0.9066593737328417,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9066593737328417,\n          0.0,\n          0.5845095378758998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Paranoid Park\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31048914410084016,\n        \"min\": 0.0,\n        \"max\": 0.8965944987152573,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          0.0925273457238931,\n          0.8965944987152573\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Helen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19211614773581748,\n        \"min\": 0.0,\n        \"max\": 0.5199288943724603,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2809781155329714,\n          0.2952525644191401,\n          0.30202994530192606\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Backdraft\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3405960734702032,\n        \"min\": 0.0,\n        \"max\": 0.8998802296119546,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.1870210946343203,\n          0.15857179931602502,\n          0.8796874270112971\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"The Murderers Are Among Us\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19211614773581748,\n        \"min\": 0.0,\n        \"max\": 0.5199288943724603,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2809781155329714,\n          0.2952525644191401,\n          0.30202994530192606\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"The Thomas Crown Affair\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2641452498610099,\n        \"min\": 0.0,\n        \"max\": 0.7320519228453545,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.478029310919969,\n          0.0,\n          0.7320519228453545\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selanjutnya kita buat fungsi rekomendasi film berdasarkan kemiripan genre  dengan menerapkan fungsi Top-N rekokemendasi"
      ],
      "metadata": {
        "id": "mg3AuxbosJSj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def film_recommendations(nama_film, similarity_data=cosine_sim_df, items=data[['movie_name', 'genres']], k=10):\n",
        "    # Mengambil data dengan menggunakan argpartition untuk melakukan partisi secara tidak langsung sepanjang sumbu yang diberikan\n",
        "    # Dataframe diubah menjadi numpy\n",
        "    # Range(start, stop, step)\n",
        "    index = similarity_data.loc[:,nama_film].to_numpy().argpartition(\n",
        "        range(-1, -k, -1))\n",
        "\n",
        "    # Mengambil data dengan similarity terbesar dari index yang ada\n",
        "    closest = similarity_data.columns[index[-1:-(k+2):-1]]\n",
        "\n",
        "    # Drop nama_film agar nama filim yang dicari tidak muncul dalam daftar rekomendasi\n",
        "    closest = closest.drop(nama_film, errors='ignore')\n",
        "\n",
        "    return pd.DataFrame(closest).merge(items).head(k)"
      ],
      "metadata": {
        "id": "RDEYHw-LkWIA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## C. Pengujian Sistem Rekomendasi"
      ],
      "metadata": {
        "id": "RTfddPGLh9Uh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada tahap ini, kita akan melakukan pengujian model yang dibuat."
      ],
      "metadata": {
        "id": "4C8LEk9Fbbo2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[data.movie_name.eq('The Man with the Golden Arm')]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "ZMv-5ufnj7oK",
        "outputId": "ecb40a83-34f9-41dd-b33c-e73c75bf16f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                   movie_name                 genres\n",
              "533  541  The Man with the Golden Arm  Crime, Drama, Romance"
            ],
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "summary": "{\n  \"name\": \"data[data\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 541,\n        \"max\": 541,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          541\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"The Man with the Golden Arm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Crime, Drama, Romance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mendapatkan rekomendasi film yang mirip dengan yang dipilih\n",
        "recommended_movies = film_recommendations('The Man with the Golden Arm')\n",
        "recommended_movies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "QLnyugXOj9GY",
        "outputId": "52b304f9-fed7-466d-bddd-29bc6560ce0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                movie_name                           genres\n",
              "0        Made in Hong Kong            Crime, Drama, Romance\n",
              "1                   3-Iron            Drama, Romance, Crime\n",
              "2  The Thomas Crown Affair  Romance, Crime, Thriller, Drama\n",
              "3  The Thomas Crown Affair            Drama, Crime, Romance\n",
              "4                B. Monkey            Romance, Crime, Drama\n",
              "5                   Schizo            Crime, Drama, Romance\n",
              "6               Angel Face            Crime, Drama, Romance\n",
              "7           Prizzi's Honor    Romance, Comedy, Crime, Drama\n",
              "8  Tie Me Up! Tie Me Down!    Comedy, Crime, Drama, Romance\n",
              "9                Music Box  Crime, Drama, Romance, Thriller"
            ],
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              "\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Thomas Crown Affair</td>\n",
              "      <td>Romance, Crime, Thriller, Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Thomas Crown Affair</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B. Monkey</td>\n",
              "      <td>Romance, Crime, Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Schizo</td>\n",
              "      <td>Crime, Drama, Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Angel Face</td>\n",
              "      <td>Crime, Drama, Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Prizzi's Honor</td>\n",
              "      <td>Romance, Comedy, Crime, Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Tie Me Up! Tie Me Down!</td>\n",
              "      <td>Comedy, Crime, Drama, Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Music Box</td>\n",
              "      <td>Crime, Drama, Romance, Thriller</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-534f1e84-0d71-434c-93a3-f0ed43a44e36')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-534f1e84-0d71-434c-93a3-f0ed43a44e36 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-534f1e84-0d71-434c-93a3-f0ed43a44e36');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f0e443d1-aeb4-45af-b39c-7ba0e9a981a9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f0e443d1-aeb4-45af-b39c-7ba0e9a981a9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9455cd7b-8c36-4ef3-aff6-ad68f58820fb\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('recommended_movies')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9455cd7b-8c36-4ef3-aff6-ad68f58820fb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('recommended_movies');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "recommended_movies",
              "summary": "{\n  \"name\": \"recommended_movies\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"movie_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Tie Me Up! Tie Me Down!\",\n          \"3-Iron\",\n          \"Angel Face\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Drama, Romance, Crime\",\n          \"Romance, Comedy, Crime, Drama\",\n          \"Crime, Drama, Romance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil yang ditampilkan akan berupa judul film yang memiliki kesaaman genre."
      ],
      "metadata": {
        "id": "xVToW0bu6AuB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## D. Evaluation"
      ],
      "metadata": {
        "id": "HLKL55nTiB9t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada tahap evaluasi model kita gunakan metrik precision untuk menghitung proporsi prediksi positif yang benar (`True Positives`) dibandingkan dengan semua prediksi positif (`True Positives` + `False Positives`)."
      ],
      "metadata": {
        "id": "6Gu0uYoOiUzp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "relevant_genres = {\"Crime\", \"Drama\", \"Romance\"}  # gunakan set agar urutan tidak penting\n",
        "\n",
        "# Fungsi untuk memeriksa kecocokan genre\n",
        "def is_relevant(genres):\n",
        "    genres_set = set(genres.split(', '))  # ubah string genre menjadi set\n",
        "    return relevant_genres.issubset(genres_set)  # periksa apakah semua genre relevan ada dalam genres_set\n",
        "\n",
        "# Tambahkan kolom apakah film relevan\n",
        "recommended_movies['is_relevant'] = recommended_movies['genres'].apply(is_relevant)\n",
        "\n",
        "# Hitung True Positives dan Total Recommended\n",
        "true_positives = recommended_movies['is_relevant'].sum()\n",
        "total_recommended = len(recommended_movies)\n",
        "\n",
        "# Precision\n",
        "precision = true_positives / total_recommended * 100\n",
        "print(f\"Precision: {precision:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtVy1C0mmPvf",
        "outputId": "a8a9c60d-9248-4fc7-87bb-9a97befb19b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil diatas, nilai metriks precesion sebesar 100.00% untuk 10 film yang direkomendasikan berdasarkan kemiripan genre."
      ],
      "metadata": {
        "id": "HCxdFQmDsoTW"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MS9hVtPxxrFd"
      },
      "source": [
        "# Collaborative Filtering (CF)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtst8ysmrzpt"
      },
      "source": [
        "Model-Based Deep Learning Collaborative Filtering adalah pendekatan yang menggabungkan teknik collaborative filtering dengan metode deep learning untuk meningkatkan akurasi dan efektivitas sistem rekomendasi. Ada beberapa langkah persiapan untuk melakukan pelatiahn model dalam tahap ini yang dapat dijelaskan sebagai berikut:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQfTQt0RthK9"
      },
      "source": [
        "## A. Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czTXpiR3sJY3"
      },
      "source": [
        "Langkah pertama, kita cek dataset kita dengan fungsi `info()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3aj3C3grMPpB",
        "outputId": "161d4ba0-e381-455d-9b4c-2d5cc3d9f643"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 20000 entries, 16328 to 24308\n",
            "Data columns (total 5 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   userId   20000 non-null  int64  \n",
            " 1   movieId  20000 non-null  int64  \n",
            " 2   rating   20000 non-null  float64\n",
            " 3   genres   20000 non-null  object \n",
            " 4   title    20000 non-null  object \n",
            "dtypes: float64(1), int64(2), object(2)\n",
            "memory usage: 937.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df_sample_final.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NWCSY8gscJn"
      },
      "source": [
        "Dari hasil diatas, terdapat 20000 baris dan 7 kolom dan memiliki 3 tipe data `float64`, 2 tipe data `int64` dan 2 tipe data `object`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWux9btVPbPU"
      },
      "source": [
        "Kedua, kita hapus kolom yang tidak dibutuhkan dalam pelatihan."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tvjPrLBpSd9K"
      },
      "outputs": [],
      "source": [
        "# Menghapus kolom 'title'\n",
        "df_sample_final = df_sample_final.drop(columns=['genres', 'title'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVzOtcKqnlp6"
      },
      "source": [
        "Langkah berikutnya kita urutkan berdasarkan kolom `userId`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "04Ib1NdunoUH"
      },
      "outputs": [],
      "source": [
        "df_sample_final = df_sample_final.sort_values('userId')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZFfiFp0tWHA"
      },
      "source": [
        "## Encoding userId dan movieId"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FE4Xspsityzw"
      },
      "source": [
        "Langkah pertama kita lakukan encoding pada `userId`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDnbv7zSVp8k",
        "outputId": "28acd9c7-88e5-4c96-bb83-7c0bcf5d9747"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "list userID:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671]\n",
            "encoded userID :  {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 12: 11, 13: 12, 14: 13, 15: 14, 16: 15, 17: 16, 18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 23, 25: 24, 26: 25, 27: 26, 28: 27, 29: 28, 30: 29, 31: 30, 32: 31, 33: 32, 34: 33, 35: 34, 36: 35, 37: 36, 38: 37, 39: 38, 40: 39, 41: 40, 42: 41, 43: 42, 44: 43, 45: 44, 46: 45, 47: 46, 48: 47, 49: 48, 50: 49, 51: 50, 52: 51, 53: 52, 54: 53, 55: 54, 56: 55, 57: 56, 58: 57, 59: 58, 60: 59, 61: 60, 62: 61, 63: 62, 64: 63, 65: 64, 66: 65, 67: 66, 68: 67, 69: 68, 70: 69, 71: 70, 72: 71, 73: 72, 74: 73, 75: 74, 76: 75, 77: 76, 78: 77, 79: 78, 80: 79, 81: 80, 82: 81, 83: 82, 84: 83, 85: 84, 86: 85, 87: 86, 88: 87, 89: 88, 90: 89, 91: 90, 92: 91, 93: 92, 94: 93, 95: 94, 96: 95, 97: 96, 98: 97, 99: 98, 100: 99, 101: 100, 102: 101, 103: 102, 104: 103, 105: 104, 106: 105, 107: 106, 108: 107, 109: 108, 110: 109, 111: 110, 112: 111, 113: 112, 114: 113, 115: 114, 116: 115, 117: 116, 118: 117, 119: 118, 120: 119, 121: 120, 122: 121, 123: 122, 124: 123, 125: 124, 126: 125, 127: 126, 128: 127, 129: 128, 130: 129, 131: 130, 132: 131, 133: 132, 134: 133, 135: 134, 136: 135, 137: 136, 138: 137, 139: 138, 140: 139, 141: 140, 142: 141, 143: 142, 144: 143, 145: 144, 146: 145, 147: 146, 148: 147, 149: 148, 150: 149, 151: 150, 152: 151, 153: 152, 154: 153, 155: 154, 156: 155, 157: 156, 158: 157, 159: 158, 160: 159, 161: 160, 162: 161, 163: 162, 164: 163, 165: 164, 166: 165, 167: 166, 168: 167, 169: 168, 170: 169, 171: 170, 172: 171, 173: 172, 174: 173, 175: 174, 176: 175, 177: 176, 178: 177, 179: 178, 180: 179, 181: 180, 182: 181, 183: 182, 184: 183, 185: 184, 186: 185, 187: 186, 188: 187, 189: 188, 190: 189, 191: 190, 192: 191, 193: 192, 194: 193, 195: 194, 196: 195, 197: 196, 198: 197, 199: 198, 200: 199, 201: 200, 202: 201, 203: 202, 204: 203, 205: 204, 206: 205, 207: 206, 208: 207, 209: 208, 210: 209, 211: 210, 212: 211, 213: 212, 214: 213, 215: 214, 216: 215, 217: 216, 218: 217, 219: 218, 220: 219, 221: 220, 222: 221, 223: 222, 224: 223, 225: 224, 226: 225, 227: 226, 228: 227, 229: 228, 230: 229, 231: 230, 232: 231, 233: 232, 234: 233, 235: 234, 236: 235, 237: 236, 238: 237, 239: 238, 240: 239, 241: 240, 242: 241, 243: 242, 244: 243, 245: 244, 246: 245, 247: 246, 248: 247, 249: 248, 250: 249, 251: 250, 252: 251, 253: 252, 254: 253, 255: 254, 256: 255, 257: 256, 258: 257, 259: 258, 260: 259, 261: 260, 262: 261, 263: 262, 264: 263, 265: 264, 266: 265, 267: 266, 268: 267, 269: 268, 270: 269, 271: 270, 272: 271, 273: 272, 274: 273, 275: 274, 276: 275, 277: 276, 278: 277, 279: 278, 280: 279, 281: 280, 282: 281, 283: 282, 284: 283, 285: 284, 286: 285, 287: 286, 288: 287, 289: 288, 290: 289, 291: 290, 292: 291, 293: 292, 294: 293, 295: 294, 296: 295, 297: 296, 298: 297, 299: 298, 300: 299, 301: 300, 302: 301, 303: 302, 304: 303, 305: 304, 306: 305, 307: 306, 308: 307, 309: 308, 310: 309, 311: 310, 312: 311, 313: 312, 314: 313, 315: 314, 316: 315, 317: 316, 318: 317, 319: 318, 320: 319, 321: 320, 322: 321, 323: 322, 324: 323, 325: 324, 326: 325, 327: 326, 328: 327, 329: 328, 330: 329, 331: 330, 332: 331, 333: 332, 334: 333, 335: 334, 336: 335, 337: 336, 338: 337, 339: 338, 340: 339, 341: 340, 342: 341, 343: 342, 344: 343, 345: 344, 346: 345, 347: 346, 348: 347, 349: 348, 350: 349, 351: 350, 352: 351, 353: 352, 354: 353, 355: 354, 356: 355, 357: 356, 358: 357, 359: 358, 360: 359, 361: 360, 362: 361, 363: 362, 364: 363, 365: 364, 366: 365, 367: 366, 368: 367, 369: 368, 370: 369, 371: 370, 372: 371, 373: 372, 374: 373, 375: 374, 376: 375, 377: 376, 378: 377, 379: 378, 380: 379, 381: 380, 382: 381, 383: 382, 384: 383, 385: 384, 386: 385, 387: 386, 388: 387, 389: 388, 390: 389, 391: 390, 392: 391, 393: 392, 394: 393, 395: 394, 396: 395, 397: 396, 398: 397, 399: 398, 400: 399, 401: 400, 402: 401, 403: 402, 404: 403, 405: 404, 406: 405, 407: 406, 408: 407, 409: 408, 410: 409, 411: 410, 412: 411, 413: 412, 414: 413, 415: 414, 416: 415, 417: 416, 418: 417, 419: 418, 420: 419, 421: 420, 422: 421, 423: 422, 424: 423, 425: 424, 426: 425, 427: 426, 428: 427, 429: 428, 430: 429, 431: 430, 432: 431, 433: 432, 434: 433, 435: 434, 436: 435, 437: 436, 438: 437, 439: 438, 440: 439, 441: 440, 442: 441, 443: 442, 444: 443, 445: 444, 446: 445, 447: 446, 448: 447, 449: 448, 450: 449, 451: 450, 452: 451, 453: 452, 454: 453, 455: 454, 456: 455, 457: 456, 458: 457, 459: 458, 460: 459, 461: 460, 462: 461, 463: 462, 464: 463, 465: 464, 466: 465, 467: 466, 468: 467, 469: 468, 470: 469, 471: 470, 472: 471, 473: 472, 474: 473, 475: 474, 476: 475, 477: 476, 478: 477, 479: 478, 480: 479, 481: 480, 482: 481, 483: 482, 484: 483, 485: 484, 486: 485, 487: 486, 488: 487, 489: 488, 490: 489, 491: 490, 492: 491, 493: 492, 494: 493, 495: 494, 496: 495, 497: 496, 498: 497, 499: 498, 500: 499, 501: 500, 502: 501, 503: 502, 504: 503, 505: 504, 506: 505, 507: 506, 508: 507, 509: 508, 510: 509, 511: 510, 512: 511, 513: 512, 514: 513, 515: 514, 516: 515, 517: 516, 518: 517, 519: 518, 520: 519, 521: 520, 522: 521, 523: 522, 524: 523, 525: 524, 526: 525, 527: 526, 528: 527, 529: 528, 530: 529, 531: 530, 532: 531, 533: 532, 534: 533, 535: 534, 536: 535, 537: 536, 538: 537, 539: 538, 540: 539, 541: 540, 542: 541, 543: 542, 544: 543, 545: 544, 546: 545, 547: 546, 548: 547, 549: 548, 550: 549, 551: 550, 552: 551, 553: 552, 554: 553, 555: 554, 556: 555, 557: 556, 558: 557, 559: 558, 560: 559, 561: 560, 562: 561, 563: 562, 564: 563, 565: 564, 566: 565, 567: 566, 568: 567, 569: 568, 570: 569, 571: 570, 572: 571, 573: 572, 574: 573, 575: 574, 576: 575, 577: 576, 578: 577, 579: 578, 580: 579, 581: 580, 582: 581, 583: 582, 584: 583, 585: 584, 586: 585, 587: 586, 588: 587, 589: 588, 590: 589, 591: 590, 592: 591, 593: 592, 594: 593, 595: 594, 596: 595, 597: 596, 598: 597, 599: 598, 600: 599, 601: 600, 602: 601, 603: 602, 604: 603, 605: 604, 606: 605, 607: 606, 608: 607, 609: 608, 610: 609, 611: 610, 612: 611, 613: 612, 614: 613, 615: 614, 616: 615, 617: 616, 618: 617, 619: 618, 620: 619, 621: 620, 622: 621, 623: 622, 624: 623, 625: 624, 626: 625, 627: 626, 628: 627, 629: 628, 630: 629, 631: 630, 632: 631, 633: 632, 634: 633, 635: 634, 636: 635, 637: 636, 638: 637, 639: 638, 640: 639, 641: 640, 642: 641, 643: 642, 644: 643, 645: 644, 646: 645, 647: 646, 648: 647, 649: 648, 650: 649, 651: 650, 652: 651, 653: 652, 654: 653, 655: 654, 656: 655, 657: 656, 658: 657, 659: 658, 660: 659, 661: 660, 662: 661, 663: 662, 664: 663, 665: 664, 666: 665, 667: 666, 668: 667, 669: 668, 670: 669, 671: 670}\n",
            "encoded angka ke userID:  {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10, 10: 11, 11: 12, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18, 18: 19, 19: 20, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 26, 26: 27, 27: 28, 28: 29, 29: 30, 30: 31, 31: 32, 32: 33, 33: 34, 34: 35, 35: 36, 36: 37, 37: 38, 38: 39, 39: 40, 40: 41, 41: 42, 42: 43, 43: 44, 44: 45, 45: 46, 46: 47, 47: 48, 48: 49, 49: 50, 50: 51, 51: 52, 52: 53, 53: 54, 54: 55, 55: 56, 56: 57, 57: 58, 58: 59, 59: 60, 60: 61, 61: 62, 62: 63, 63: 64, 64: 65, 65: 66, 66: 67, 67: 68, 68: 69, 69: 70, 70: 71, 71: 72, 72: 73, 73: 74, 74: 75, 75: 76, 76: 77, 77: 78, 78: 79, 79: 80, 80: 81, 81: 82, 82: 83, 83: 84, 84: 85, 85: 86, 86: 87, 87: 88, 88: 89, 89: 90, 90: 91, 91: 92, 92: 93, 93: 94, 94: 95, 95: 96, 96: 97, 97: 98, 98: 99, 99: 100, 100: 101, 101: 102, 102: 103, 103: 104, 104: 105, 105: 106, 106: 107, 107: 108, 108: 109, 109: 110, 110: 111, 111: 112, 112: 113, 113: 114, 114: 115, 115: 116, 116: 117, 117: 118, 118: 119, 119: 120, 120: 121, 121: 122, 122: 123, 123: 124, 124: 125, 125: 126, 126: 127, 127: 128, 128: 129, 129: 130, 130: 131, 131: 132, 132: 133, 133: 134, 134: 135, 135: 136, 136: 137, 137: 138, 138: 139, 139: 140, 140: 141, 141: 142, 142: 143, 143: 144, 144: 145, 145: 146, 146: 147, 147: 148, 148: 149, 149: 150, 150: 151, 151: 152, 152: 153, 153: 154, 154: 155, 155: 156, 156: 157, 157: 158, 158: 159, 159: 160, 160: 161, 161: 162, 162: 163, 163: 164, 164: 165, 165: 166, 166: 167, 167: 168, 168: 169, 169: 170, 170: 171, 171: 172, 172: 173, 173: 174, 174: 175, 175: 176, 176: 177, 177: 178, 178: 179, 179: 180, 180: 181, 181: 182, 182: 183, 183: 184, 184: 185, 185: 186, 186: 187, 187: 188, 188: 189, 189: 190, 190: 191, 191: 192, 192: 193, 193: 194, 194: 195, 195: 196, 196: 197, 197: 198, 198: 199, 199: 200, 200: 201, 201: 202, 202: 203, 203: 204, 204: 205, 205: 206, 206: 207, 207: 208, 208: 209, 209: 210, 210: 211, 211: 212, 212: 213, 213: 214, 214: 215, 215: 216, 216: 217, 217: 218, 218: 219, 219: 220, 220: 221, 221: 222, 222: 223, 223: 224, 224: 225, 225: 226, 226: 227, 227: 228, 228: 229, 229: 230, 230: 231, 231: 232, 232: 233, 233: 234, 234: 235, 235: 236, 236: 237, 237: 238, 238: 239, 239: 240, 240: 241, 241: 242, 242: 243, 243: 244, 244: 245, 245: 246, 246: 247, 247: 248, 248: 249, 249: 250, 250: 251, 251: 252, 252: 253, 253: 254, 254: 255, 255: 256, 256: 257, 257: 258, 258: 259, 259: 260, 260: 261, 261: 262, 262: 263, 263: 264, 264: 265, 265: 266, 266: 267, 267: 268, 268: 269, 269: 270, 270: 271, 271: 272, 272: 273, 273: 274, 274: 275, 275: 276, 276: 277, 277: 278, 278: 279, 279: 280, 280: 281, 281: 282, 282: 283, 283: 284, 284: 285, 285: 286, 286: 287, 287: 288, 288: 289, 289: 290, 290: 291, 291: 292, 292: 293, 293: 294, 294: 295, 295: 296, 296: 297, 297: 298, 298: 299, 299: 300, 300: 301, 301: 302, 302: 303, 303: 304, 304: 305, 305: 306, 306: 307, 307: 308, 308: 309, 309: 310, 310: 311, 311: 312, 312: 313, 313: 314, 314: 315, 315: 316, 316: 317, 317: 318, 318: 319, 319: 320, 320: 321, 321: 322, 322: 323, 323: 324, 324: 325, 325: 326, 326: 327, 327: 328, 328: 329, 329: 330, 330: 331, 331: 332, 332: 333, 333: 334, 334: 335, 335: 336, 336: 337, 337: 338, 338: 339, 339: 340, 340: 341, 341: 342, 342: 343, 343: 344, 344: 345, 345: 346, 346: 347, 347: 348, 348: 349, 349: 350, 350: 351, 351: 352, 352: 353, 353: 354, 354: 355, 355: 356, 356: 357, 357: 358, 358: 359, 359: 360, 360: 361, 361: 362, 362: 363, 363: 364, 364: 365, 365: 366, 366: 367, 367: 368, 368: 369, 369: 370, 370: 371, 371: 372, 372: 373, 373: 374, 374: 375, 375: 376, 376: 377, 377: 378, 378: 379, 379: 380, 380: 381, 381: 382, 382: 383, 383: 384, 384: 385, 385: 386, 386: 387, 387: 388, 388: 389, 389: 390, 390: 391, 391: 392, 392: 393, 393: 394, 394: 395, 395: 396, 396: 397, 397: 398, 398: 399, 399: 400, 400: 401, 401: 402, 402: 403, 403: 404, 404: 405, 405: 406, 406: 407, 407: 408, 408: 409, 409: 410, 410: 411, 411: 412, 412: 413, 413: 414, 414: 415, 415: 416, 416: 417, 417: 418, 418: 419, 419: 420, 420: 421, 421: 422, 422: 423, 423: 424, 424: 425, 425: 426, 426: 427, 427: 428, 428: 429, 429: 430, 430: 431, 431: 432, 432: 433, 433: 434, 434: 435, 435: 436, 436: 437, 437: 438, 438: 439, 439: 440, 440: 441, 441: 442, 442: 443, 443: 444, 444: 445, 445: 446, 446: 447, 447: 448, 448: 449, 449: 450, 450: 451, 451: 452, 452: 453, 453: 454, 454: 455, 455: 456, 456: 457, 457: 458, 458: 459, 459: 460, 460: 461, 461: 462, 462: 463, 463: 464, 464: 465, 465: 466, 466: 467, 467: 468, 468: 469, 469: 470, 470: 471, 471: 472, 472: 473, 473: 474, 474: 475, 475: 476, 476: 477, 477: 478, 478: 479, 479: 480, 480: 481, 481: 482, 482: 483, 483: 484, 484: 485, 485: 486, 486: 487, 487: 488, 488: 489, 489: 490, 490: 491, 491: 492, 492: 493, 493: 494, 494: 495, 495: 496, 496: 497, 497: 498, 498: 499, 499: 500, 500: 501, 501: 502, 502: 503, 503: 504, 504: 505, 505: 506, 506: 507, 507: 508, 508: 509, 509: 510, 510: 511, 511: 512, 512: 513, 513: 514, 514: 515, 515: 516, 516: 517, 517: 518, 518: 519, 519: 520, 520: 521, 521: 522, 522: 523, 523: 524, 524: 525, 525: 526, 526: 527, 527: 528, 528: 529, 529: 530, 530: 531, 531: 532, 532: 533, 533: 534, 534: 535, 535: 536, 536: 537, 537: 538, 538: 539, 539: 540, 540: 541, 541: 542, 542: 543, 543: 544, 544: 545, 545: 546, 546: 547, 547: 548, 548: 549, 549: 550, 550: 551, 551: 552, 552: 553, 553: 554, 554: 555, 555: 556, 556: 557, 557: 558, 558: 559, 559: 560, 560: 561, 561: 562, 562: 563, 563: 564, 564: 565, 565: 566, 566: 567, 567: 568, 568: 569, 569: 570, 570: 571, 571: 572, 572: 573, 573: 574, 574: 575, 575: 576, 576: 577, 577: 578, 578: 579, 579: 580, 580: 581, 581: 582, 582: 583, 583: 584, 584: 585, 585: 586, 586: 587, 587: 588, 588: 589, 589: 590, 590: 591, 591: 592, 592: 593, 593: 594, 594: 595, 595: 596, 596: 597, 597: 598, 598: 599, 599: 600, 600: 601, 601: 602, 602: 603, 603: 604, 604: 605, 605: 606, 606: 607, 607: 608, 608: 609, 609: 610, 610: 611, 611: 612, 612: 613, 613: 614, 614: 615, 615: 616, 616: 617, 617: 618, 618: 619, 619: 620, 620: 621, 621: 622, 622: 623, 623: 624, 624: 625, 625: 626, 626: 627, 627: 628, 628: 629, 629: 630, 630: 631, 631: 632, 632: 633, 633: 634, 634: 635, 635: 636, 636: 637, 637: 638, 638: 639, 639: 640, 640: 641, 641: 642, 642: 643, 643: 644, 644: 645, 645: 646, 646: 647, 647: 648, 648: 649, 649: 650, 650: 651, 651: 652, 652: 653, 653: 654, 654: 655, 655: 656, 656: 657, 657: 658, 658: 659, 659: 660, 660: 661, 661: 662, 662: 663, 663: 664, 664: 665, 665: 666, 666: 667, 667: 668, 668: 669, 669: 670, 670: 671}\n"
          ]
        }
      ],
      "source": [
        "# Mengubah userID menjadi list tanpa nilai yang sama\n",
        "user_ids = df_sample_final['userId'].unique().tolist()\n",
        "print('list userID: ', user_ids)\n",
        "\n",
        "# Melakukan encoding userID\n",
        "user_to_user_encoded = {x: i for i, x in enumerate(user_ids)}\n",
        "print('encoded userID : ', user_to_user_encoded)\n",
        "\n",
        "# Melakukan proses encoding angka ke ke userID\n",
        "user_encoded_to_user = {i: x for i, x in enumerate(user_ids)}\n",
        "print('encoded angka ke userID: ', user_encoded_to_user)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CLV5yF7V1lx"
      },
      "source": [
        "Langkah kedua, lakukan hal yang sama pada `movieId`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6O9cY0GHV4Hq",
        "outputId": "3698aa91-6805-4fa3-8b42-cbb63e20f1e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "encoded angka ke movieId:  {0: 2105, 1: 1405, 2: 2193, 3: 500, 4: 539, 5: 585, 6: 168, 7: 317, 8: 235, 9: 165, 10: 349, 11: 319, 12: 350, 13: 454, 14: 339, 15: 457, 16: 153, 17: 248, 18: 405, 19: 144, 20: 272, 21: 62, 22: 661, 23: 497, 24: 225, 25: 265, 26: 150, 27: 110, 28: 2028, 29: 296, 30: 318, 31: 1884, 32: 247, 33: 778, 34: 2694, 35: 1580, 36: 267, 37: 592, 38: 58559, 39: 377, 40: 442, 41: 1396, 42: 858, 43: 2788, 44: 1544, 45: 1377, 46: 480, 47: 1372, 48: 3060, 49: 1278, 50: 2085, 51: 2094, 52: 541, 53: 440, 54: 2110, 55: 410, 56: 329, 57: 2102, 58: 2348, 59: 1374, 60: 2114, 61: 1961, 62: 464, 63: 2100, 64: 1968, 65: 596, 66: 434, 67: 2109, 68: 910, 69: 1257, 70: 2087, 71: 431, 72: 2640, 73: 185, 74: 435, 75: 3034, 76: 2115, 77: 919, 78: 2143, 79: 1376, 80: 2054, 81: 3101, 82: 112, 83: 1265, 84: 616, 85: 2144, 86: 33166, 87: 1997, 88: 364, 89: 6373, 90: 595, 91: 41569, 92: 344, 93: 903, 94: 4025, 95: 5995, 96: 40819, 97: 2355, 98: 5816, 99: 3, 100: 586, 101: 231, 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          ]
        }
      ],
      "source": [
        "# Mengubah placeID menjadi list tanpa nilai yang sama\n",
        "movie_ids = df_sample_final['movieId'].unique().tolist()\n",
        "print('list movieId: ', movie_ids)\n",
        "\n",
        "# Melakukan proses encoding placeID\n",
        "movie_to_movie_encoded = {x: i for i, x in enumerate(movie_ids)}\n",
        "print('encoded movieId : ', movie_to_movie_encoded)\n",
        "\n",
        "# Melakukan proses encoding angka ke placeID\n",
        "movie_encoded_to_movie = {i: x for i, x in enumerate(movie_ids)}\n",
        "print('encoded angka ke movieId: ', movie_encoded_to_movie)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLy3v_MqWHCk"
      },
      "source": [
        "Setelah proses encoding selesai, kita petakan `userId` dan `movieId` ke dataframe yang berkaitan."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ipbF_LvHWJaH"
      },
      "outputs": [],
      "source": [
        "# Mapping userID ke dataframe user\n",
        "df_sample_final['user'] = df_sample_final['userId'].map(user_to_user_encoded)\n",
        "\n",
        "# Mapping placeID ke dataframe movie\n",
        "df_sample_final['movie'] = df_sample_final['movieId'].map(movie_to_movie_encoded)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0AlprV9uQ8p"
      },
      "source": [
        "Selanjutnya kita ambil total_user, total movie dan nilai rating minimum dan maksimum untuk proses pembagian dataset sebelum melakukan pelatihan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56C_a_pHPbdZ",
        "outputId": "2d1e8399-cbb3-4c64-e38d-50bf9afc2c3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "671\n",
            "2249\n",
            "Number of User: 671, Number of Movie: 2249, Min Rating: 0.5, Max Rating: 5.0\n"
          ]
        }
      ],
      "source": [
        "# Mendapatkan jumlah user\n",
        "total_user = len(user_to_user_encoded)\n",
        "print(total_user)\n",
        "\n",
        "# Mendapatkan jumlah movie\n",
        "total_movies = len(movie_encoded_to_movie)\n",
        "print(total_movies)\n",
        "\n",
        "# Mengubah rating menjadi nilai float\n",
        "df_sample_final['rating'] = df_sample_final['rating'].values.astype(np.float32)\n",
        "\n",
        "# Nilai minimum rating\n",
        "min_rating = min(df_sample_final['rating'])\n",
        "\n",
        "# Nilai maksimal rating\n",
        "max_rating = max(df_sample_final['rating'])\n",
        "\n",
        "print('Number of User: {}, Number of Movie: {}, Min Rating: {}, Max Rating: {}'.format(\n",
        "    total_user, total_movies, min_rating, max_rating\n",
        "))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQiAPF7qutGq"
      },
      "source": [
        "Dapat dilihat hasilnya, kita memiliki 669 user, 2277 movie dan rating minimim 0.5 dan rating maksimum 5.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDj0OdKoR7Rw"
      },
      "source": [
        "### Membagi Data untuk Training dan Validasi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihKmqi65u75e"
      },
      "source": [
        "Pada tahap ini kita membagi data training dan data validasi untuk proses pelatihan model. Namusn sebelum itu kita perlu mengacak dataset kita sehingga menjadi data yang valid."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ArZZcSppR8cg",
        "outputId": "3ead1314-ebf6-4066-e290-4d951a7571f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       userId  movieId  rating  user  movie\n",
              "24104     385      500     4.0   384      3\n",
              "4975       73     6373     2.5    72     89\n",
              "19984     311     2078     4.0   310    815\n",
              "2217       30     4929     3.0    29    661\n",
              "31331     483    58559     4.0   482     38\n",
              "...       ...      ...     ...   ...    ...\n",
              "25445     405     3577     4.5   404    361\n",
              "27053     431      215     5.0   430    468\n",
              "12317     195     3077     4.0   194   1329\n",
              "1857       23      605     4.0    22    553\n",
              "36249     547     3028     3.0   546   1427\n",
              "\n",
              "[20000 rows x 5 columns]"
            ],
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7fcc7e9e-6c7c-4b7a-821e-023e7dc4e044 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_31aaa9fb-2b08-4251-9fb2-9f4dc2ed74ec\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_sample_final')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_31aaa9fb-2b08-4251-9fb2-9f4dc2ed74ec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_sample_final');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_sample_final",
              "summary": "{\n  \"name\": \"df_sample_final\",\n  \"rows\": 20000,\n  \"fields\": [\n    {\n      \"column\": \"userId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195,\n        \"min\": 1,\n        \"max\": 671,\n        \"num_unique_values\": 671,\n        \"samples\": [\n          225,\n          242,\n          153\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15977,\n        \"min\": 2,\n        \"max\": 160718,\n        \"num_unique_values\": 2249,\n        \"samples\": [\n          992,\n          2057,\n          78174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.5,\n          2.5,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195,\n        \"min\": 0,\n        \"max\": 670,\n        \"num_unique_values\": 671,\n        \"samples\": [\n          224,\n          241,\n          152\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 497,\n        \"min\": 0,\n        \"max\": 2248,\n        \"num_unique_values\": 2249,\n        \"samples\": [\n          1627,\n          2176,\n          2198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "# Mengacak dataset\n",
        "df_sample_final = df_sample_final.sample(frac=1, random_state=42)\n",
        "df_sample_final"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAWEmSSMvVcC"
      },
      "source": [
        "Selanjutnya kita buat variabel x untuk mencocokkan data user dan Movie menjadi satu value, kemudian variabel y untuk membuat rating dari hasil. Terakhir kita Membagi menjadi 80% data train dan 20% data validasi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PllqFfizIutZ",
        "outputId": "af19d2ff-e1b6-4d81-db15-237312d5d011"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 384    3]\n",
            " [  72   89]\n",
            " [ 310  815]\n",
            " ...\n",
            " [ 194 1329]\n",
            " [  22  553]\n",
            " [ 546 1427]] [0.77777778 0.44444444 0.77777778 ... 0.77777778 0.77777778 0.55555556]\n"
          ]
        }
      ],
      "source": [
        "x = df_sample_final[['user', 'movie']].values\n",
        "\n",
        "y = df_sample_final['rating'].apply(lambda x: (x - min_rating) / (max_rating - min_rating)).values\n",
        "\n",
        "train_indices = int(0.8 * df_sample_final.shape[0])\n",
        "x_train, x_val, y_train, y_val = (\n",
        "    x[:train_indices],\n",
        "    x[train_indices:],\n",
        "    y[:train_indices],\n",
        "    y[train_indices:]\n",
        ")\n",
        "\n",
        "print(x, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CgfVfIF1EKD4"
      },
      "source": [
        "## B. Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_kPYvhWT7NP"
      },
      "source": [
        "Pada tahap ini, model menghitung skor kecocokan antara user dan movie teknik embedding. Pertama, kita melakukan proses embedding terhadap data user dan movie. Selanjutnya, lakukan operasi perkalian dot product antara embedding user dan movie. Selain itu, kita juga dapat menambahkan bias untuk setiap user dan movie. Skor kecocokan ditetapkan dalam skala [0,1] dengan fungsi aktivasi sigmoid. Di sini, kita membuat class RecommenderNet dengan keras Model class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PKs_3zzjT7ba"
      },
      "outputs": [],
      "source": [
        "class RecommenderNet(tf.keras.Model):\n",
        "  # Insialisasi fungsi\n",
        "  def __init__(self, total_user, total_movies, embedding_size, **kwargs):\n",
        "    super(RecommenderNet, self).__init__(**kwargs)\n",
        "    self.total_user = total_user\n",
        "    self.total_movies = total_movies\n",
        "    self.embedding_size = embedding_size\n",
        "    self.user_embedding = layers.Embedding( # layer embedding user\n",
        "        total_user,\n",
        "        embedding_size,\n",
        "        embeddings_initializer = 'he_normal',\n",
        "        embeddings_regularizer = keras.regularizers.l2(1e-6)\n",
        "    )\n",
        "    self.user_bias = layers.Embedding(total_user, 1) # layer embedding user bias\n",
        "    self.movie_embedding = layers.Embedding( # layer embeddings movie\n",
        "        total_movies,\n",
        "        embedding_size,\n",
        "        embeddings_initializer = 'he_normal',\n",
        "        embeddings_regularizer = keras.regularizers.l2(1e-6)\n",
        "    )\n",
        "    self.movie_bias = layers.Embedding(total_movies, 1) # layer embedding movie bias\n",
        "\n",
        "  def call(self, inputs):\n",
        "    user_vector = self.user_embedding(inputs[:,0]) # memanggil layer embedding 1\n",
        "    user_bias = self.user_bias(inputs[:, 0]) # memanggil layer embedding 2\n",
        "    movie_vector = self.movie_embedding(inputs[:, 1]) # memanggil layer embedding 3\n",
        "    movie_bias = self.movie_bias(inputs[:, 1]) # memanggil layer embedding 4\n",
        "\n",
        "    dot_user_movie = tf.tensordot(user_vector, movie_vector, 2)\n",
        "\n",
        "    x = dot_user_movie + user_bias + movie_bias\n",
        "\n",
        "    return tf.nn.sigmoid(x) # activation sigmoid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lUpCPR7URai"
      },
      "source": [
        "Selanjutnya, lakukan proses compile terhadap model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2WRegJlQUR7m"
      },
      "outputs": [],
      "source": [
        "model = RecommenderNet(total_user, total_movies, 50) # inisialisasi model\n",
        "\n",
        "# model compile\n",
        "model.compile(\n",
        "    loss=tf.keras.losses.MeanSquaredError(),\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "    metrics=[\n",
        "        tf.keras.metrics.MeanAbsoluteError(name=\"mean_absolute_error\"),\n",
        "        tf.keras.metrics.RootMeanSquaredError(name=\"root_mean_squared_error\")\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrSSh-gbUdAl"
      },
      "source": [
        "Model ini menggunakan Binary Crossentropy untuk menghitung loss function, Adam (Adaptive Moment Estimation) sebagai optimizer, dan root mean squared error (RMSE) sebagai metrics evaluation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pnODZXRUhF9"
      },
      "source": [
        "Langkah berikutnya, mulailah proses training. Pada proses ini kita gunakan fungsi `callbacks`, dimana jika kinerja model tidak mengalami keanaikan maka pelatiahan dihentikan. Pada proses training parameter yang digunakan yakni `batch_size=8`, `epoch = 50`, `shuffle = True` dan `verbose=1`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPHOjNaRUhqR",
        "outputId": "a5902620-00fc-4d16-cbff-cd19ded11328"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 2ms/step - loss: 0.0791 - mean_absolute_error: 0.2363 - root_mean_squared_error: 0.2808 - val_loss: 0.0498 - val_mean_absolute_error: 0.1793 - val_root_mean_squared_error: 0.2226\n",
            "Epoch 2/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 2ms/step - loss: 0.0495 - mean_absolute_error: 0.1763 - root_mean_squared_error: 0.2219 - val_loss: 0.0478 - val_mean_absolute_error: 0.1758 - val_root_mean_squared_error: 0.2179\n",
            "Epoch 3/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - loss: 0.0438 - mean_absolute_error: 0.1651 - root_mean_squared_error: 0.2084 - val_loss: 0.0452 - val_mean_absolute_error: 0.1689 - val_root_mean_squared_error: 0.2115\n",
            "Epoch 4/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - loss: 0.0407 - mean_absolute_error: 0.1572 - root_mean_squared_error: 0.2005 - val_loss: 0.0441 - val_mean_absolute_error: 0.1660 - val_root_mean_squared_error: 0.2087\n",
            "Epoch 5/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0393 - mean_absolute_error: 0.1545 - root_mean_squared_error: 0.1970 - val_loss: 0.0434 - val_mean_absolute_error: 0.1646 - val_root_mean_squared_error: 0.2069\n",
            "Epoch 6/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0378 - mean_absolute_error: 0.1507 - root_mean_squared_error: 0.1928 - val_loss: 0.0429 - val_mean_absolute_error: 0.1630 - val_root_mean_squared_error: 0.2057\n",
            "Epoch 7/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - loss: 0.0370 - mean_absolute_error: 0.1492 - root_mean_squared_error: 0.1907 - val_loss: 0.0428 - val_mean_absolute_error: 0.1624 - val_root_mean_squared_error: 0.2053\n",
            "Epoch 8/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0354 - mean_absolute_error: 0.1449 - root_mean_squared_error: 0.1863 - val_loss: 0.0426 - val_mean_absolute_error: 0.1615 - val_root_mean_squared_error: 0.2048\n",
            "Epoch 9/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - loss: 0.0350 - mean_absolute_error: 0.1449 - root_mean_squared_error: 0.1852 - val_loss: 0.0425 - val_mean_absolute_error: 0.1612 - val_root_mean_squared_error: 0.2045\n",
            "Epoch 10/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - loss: 0.0343 - mean_absolute_error: 0.1435 - root_mean_squared_error: 0.1833 - val_loss: 0.0428 - val_mean_absolute_error: 0.1618 - val_root_mean_squared_error: 0.2051\n",
            "Epoch 11/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0335 - mean_absolute_error: 0.1415 - root_mean_squared_error: 0.1812 - val_loss: 0.0425 - val_mean_absolute_error: 0.1611 - val_root_mean_squared_error: 0.2044\n",
            "Epoch 12/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0331 - mean_absolute_error: 0.1401 - root_mean_squared_error: 0.1800 - val_loss: 0.0422 - val_mean_absolute_error: 0.1601 - val_root_mean_squared_error: 0.2036\n",
            "Epoch 13/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 0.0327 - mean_absolute_error: 0.1399 - root_mean_squared_error: 0.1789 - val_loss: 0.0424 - val_mean_absolute_error: 0.1611 - val_root_mean_squared_error: 0.2041\n",
            "Epoch 14/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0322 - mean_absolute_error: 0.1384 - root_mean_squared_error: 0.1774 - val_loss: 0.0424 - val_mean_absolute_error: 0.1609 - val_root_mean_squared_error: 0.2041\n",
            "Epoch 15/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - loss: 0.0318 - mean_absolute_error: 0.1379 - root_mean_squared_error: 0.1762 - val_loss: 0.0425 - val_mean_absolute_error: 0.1612 - val_root_mean_squared_error: 0.2044\n",
            "Epoch 16/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - loss: 0.0320 - mean_absolute_error: 0.1382 - root_mean_squared_error: 0.1769 - val_loss: 0.0427 - val_mean_absolute_error: 0.1615 - val_root_mean_squared_error: 0.2049\n",
            "Epoch 17/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0315 - mean_absolute_error: 0.1365 - root_mean_squared_error: 0.1755 - val_loss: 0.0423 - val_mean_absolute_error: 0.1605 - val_root_mean_squared_error: 0.2040\n",
            "Epoch 18/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.0315 - mean_absolute_error: 0.1368 - root_mean_squared_error: 0.1755 - val_loss: 0.0422 - val_mean_absolute_error: 0.1601 - val_root_mean_squared_error: 0.2037\n",
            "Epoch 19/50\n",
            "\u001b[1m2000/2000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - loss: 0.0317 - mean_absolute_error: 0.1374 - root_mean_squared_error: 0.1760 - val_loss: 0.0422 - val_mean_absolute_error: 0.1601 - val_root_mean_squared_error: 0.2038\n"
          ]
        }
      ],
      "source": [
        "callbacks = EarlyStopping(\n",
        "    min_delta=0.0001,\n",
        "    patience=7,\n",
        "    restore_best_weights=True,\n",
        ")\n",
        "\n",
        "# Memulai training\n",
        "history = model.fit(\n",
        "    x = x_train,\n",
        "    y = y_train,\n",
        "    batch_size = 8,\n",
        "    shuffle = True,\n",
        "    epochs = 50,\n",
        "    validation_data = (x_val, y_val),\n",
        "    verbose=1,\n",
        "    callbacks=[callbacks]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bfaByPmxXwz"
      },
      "source": [
        "Dapat dilihat, hasil pelatiahn memperoleh nilai mean_absolute_error: 0.1374 dan root_mean_squared_error: 0.1760"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuQAqll0xJ8W"
      },
      "source": [
        "## C. Pengujian Rekomendasi Movie CF"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RW8uVlsvxK_y"
      },
      "source": [
        "Sebelumnya, pengguna telah memberi rating pada beberapa film yang telah mereka nonton. Kita menggunakan rating ini untuk membuat rekomendasi film yang mungkin cocok untuk pengguna."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFujHxBqx-eC"
      },
      "outputs": [],
      "source": [
        "movies_df = movies_new\n",
        "df = pd.read_csv('/content/drive/MyDrive/Dataset/MoviesDataset/ratings_small.csv')\n",
        "\n",
        "# Mengambil sample user\n",
        "user_id = df.userId.sample(1).iloc[0]\n",
        "movie_watch_by_user = df[df.userId == user_id]\n",
        "\n",
        "# Operator bitwise (~), bisa diketahui di sini https://docs.python.org/3/reference/expressions.html\n",
        "movie_not_watch = movies_df[~movies_df['id'].isin(movie_watch_by_user.movieId.values)]['id']\n",
        "movie_not_watch = list(\n",
        "    set(movie_not_watch)\n",
        "    .intersection(set(movie_to_movie_encoded.keys()))\n",
        ")\n",
        "\n",
        "movie_not_watch = [[movie_to_movie_encoded.get(x)] for x in movie_not_watch]\n",
        "user_encoder = user_to_user_encoded.get(user_id)\n",
        "user_movie_array = np.hstack(\n",
        "    ([[user_encoder]] * len(movie_not_watch), movie_not_watch)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2wKSXY_5PcW"
      },
      "source": [
        "Selanjutnya, untuk memperoleh rekomendasi film, gunakan fungsi `model.predict()` dari library Keras dengan menerapkan kode berikut."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2UGMQN75PAU",
        "outputId": "9a5887f1-404a-4cff-c23a-f50e142c5e78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step\n",
            "Rekomendasi Film untuk Pengguna dengan ID: 189\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Film dengan rating tertinggi untuk pengguna:\n",
            "--------------------------------------------------------------------------------\n",
            "Young and Innocent : Drama, Crime\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "Rekomendasi 10 Film Terbaik:\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 1\n",
            "Nama Film  : Cars\n",
            "Genre      : Animation, Adventure, Comedy, Family\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 2\n",
            "Nama Film  : The Thomas Crown Affair\n",
            "Genre      : Romance, Crime, Thriller, Drama\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 3\n",
            "Nama Film  : Doctor Zhivago\n",
            "Genre      : Drama, Romance, War\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 4\n",
            "Nama Film  : Galaxy Quest\n",
            "Genre      : Comedy, Family, Science Fiction\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 5\n",
            "Nama Film  : Dead Man\n",
            "Genre      : Drama, Fantasy, Western\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 6\n",
            "Nama Film  : Dawn of the Dead\n",
            "Genre      : Horror\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 7\n",
            "Nama Film  : Cool Hand Luke\n",
            "Genre      : Crime, Drama\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 8\n",
            "Nama Film  : The Bank Dick\n",
            "Genre      : Comedy\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 9\n",
            "Nama Film  : Birdman of Alcatraz\n",
            "Genre      : Drama\n",
            "--------------------------------------------------------------------------------\n",
            "--------------------------------------------------------------------------------\n",
            "No         : 10\n",
            "Nama Film  : Pandora's Box\n",
            "Genre      : Drama, Thriller, Romance\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "ratings_pre = model.predict(user_movie_array).flatten()\n",
        "\n",
        "top_ratings_indices = ratings_pre.argsort()[-10:][::-1]\n",
        "recommended_movie_ids = [\n",
        "    movie_encoded_to_movie.get(movie_not_watch[x][0]) for x in top_ratings_indices\n",
        "]\n",
        "\n",
        "print('Rekomendasi Film untuk Pengguna dengan ID: {}'.format(user_id))\n",
        "print()\n",
        "print('----' * 20)\n",
        "print('Film dengan rating tertinggi untuk pengguna:')\n",
        "print('----' * 20)\n",
        "\n",
        "top_movie_user = (\n",
        "    movie_watch_by_user.sort_values(\n",
        "        by = 'rating',\n",
        "        ascending=False\n",
        "    )\n",
        "    .head(5)\n",
        "    .movieId.values\n",
        ")\n",
        "\n",
        "movie_df_rows = movies_df[movies_df['id'].isin(top_movie_user)]\n",
        "for row in movie_df_rows.itertuples():\n",
        "    print(row.movie_name, ':', row.genres)\n",
        "\n",
        "print()\n",
        "print('----' * 20)\n",
        "print('Rekomendasi 10 Film Terbaik:')\n",
        "print('----' * 20)\n",
        "\n",
        "no=1\n",
        "recommended_movie = movies_df[movies_df['id'].isin(recommended_movie_ids)]\n",
        "for row in recommended_movie.itertuples():\n",
        "  print('----' * 20)\n",
        "  print('No         :', no)\n",
        "  print('Nama Film  :', row.movie_name)\n",
        "  print('Genre      :', row.genres)\n",
        "  print('----' * 20)\n",
        "  no += 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PecUtE4owHZe"
      },
      "source": [
        "## D. Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualisasi Metriks"
      ],
      "metadata": {
        "id": "AY8_fhbloLbG"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaHh1UP_xn96"
      },
      "source": [
        "Pada tahap ini kita akana lakukan visualisasi metrik seperti **Mean Absolute Error (MAE)** dan **Root Mean Squared Error (RMSE)**. Kedua metrik ini sangat penting dalam mengevaluasi kinerja model prediksi. Kedua metrik ini memberikan informasi tentang seberapa baik model dapat memprediksi nilai aktual, dan visualisasi dapat membantu dalam memahami perbandingan antara keduanya serta tren kesalahan dari waktu ke waktu."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZ8tKjqkfafE"
      },
      "source": [
        "#### Mean Absolute Error Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "BX7BoTaqwTjI",
        "outputId": "76560415-f04a-453a-e86b-301b1b6411ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['mean_absolute_error'])\n",
        "plt.plot(history.history['val_mean_absolute_error'])\n",
        "plt.title('model_metrics')\n",
        "plt.ylabel('mean_absolute_error')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['mean_absolute_error', 'val_mean_absolute_error'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKs9_M2qykul"
      },
      "source": [
        "Berdasarkan hasil `fitting` nilai konvergen metrik MAE berada sedikit dibawah 0.1374 untuk training dan sedikit diatas 0.1500 untuk validasi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1hM7hlXfnzZ"
      },
      "source": [
        "#### Root Mean Squared Error Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZVI8pG3Ofs4M",
        "outputId": "b7ba5a74-fc87-48b0-aa74-c2367cbdc2cd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['root_mean_squared_error'])\n",
        "plt.plot(history.history['val_root_mean_squared_error'])\n",
        "plt.title('model_metrics')\n",
        "plt.ylabel('root_mean_squared_error')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['root_mean_squared_error', 'val_root_mean_squared_error'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7P17PRHqzBOU"
      },
      "source": [
        "Berdasarkan hasil fitting nilai konvergen metrik RMSE berada sedikit diatas 0.1760 untuk training dan sedikit dibawah 0.180 untuk validasi."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "collapsed_sections": [
        "jQfTQt0RthK9"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}