{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lL1VU3_RbjFM",
        "outputId": "85bb8227-b9c3-4f2b-8023-84994e00f573"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow-text\n",
            "  Downloading tensorflow_text-2.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tensorflow-hub>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-text) (0.13.0)\n",
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            "Requirement already satisfied: scipy>=1.7 in /usr/local/lib/python3.10/dist-packages (from jax>=0.3.15->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.10.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.0.0)\n",
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            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.27.1)\n",
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            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.8.1)\n",
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            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.3.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (3.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (2.1.2)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow<2.13,>=2.12.0->tensorflow-text) (3.2.2)\n",
            "Installing collected packages: tensorflow-text\n",
            "Successfully installed tensorflow-text-2.12.1\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBx_2AmVQ51R",
        "outputId": "76f21b88-d568-4348-ca9d-cec16e44bff1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jxbcGuCnb5Ji",
        "outputId": "9fa970e3-3087-45c2-fec1-3743378be36b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Text  Class\n",
              "0  I am a Full-Stack Web Developer who is very in...      0\n",
              "1  A person who passionate about software develop...      0\n",
              "2  Hi, saya adalah seorang web developer saya men...      0\n",
              "3  Introducing, a Fresh Graduate of Associate deg...      0\n",
              "4  Hi, my name is Octavian Yudha Mahendra, you ca...      2"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>Text</th>\n",
              "      <th>Class</th>\n",
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              "      <th>0</th>\n",
              "      <td>I am a Full-Stack Web Developer who is very in...</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>A person who passionate about software develop...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hi, saya adalah seorang web developer saya men...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Introducing, a Fresh Graduate of Associate deg...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hi, my name is Octavian Yudha Mahendra, you ca...</td>\n",
              "      <td>2</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebd2acd8-114b-44a9-8232-1f44fe15b785')\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ebd2acd8-114b-44a9-8232-1f44fe15b785 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-ebd2acd8-114b-44a9-8232-1f44fe15b785');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/drive/MyDrive/linkedIn4.csv', sep=';')\n",
        "df = df.dropna(axis=1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33oSW3pU5hro"
      },
      "source": [
        "Kode di ini akan membuat plot horizontal bar yang menampilkan jumlah dan persentase ulasan untuk setiap kelas dalam kolom ‘Class’ dari dataset yang kita punya."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "id": "2KumwLPAcLn0",
        "outputId": "08289818-8ac9-4e77-92fc-1fa831120d53"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Mengimport modul numpy dan matplotlib.pyplot, dan mengatur gaya plot ke ggplot\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "# Menghitung jumlah kelas yang berbeda pada kolom \"Class\" pada dataset kamu\n",
        "num_classes = len(df[\"Class\"].value_counts())\n",
        "\n",
        "# Membuat array warna berdasarkan jumlah kelas yang berbeda\n",
        "colors = plt.cm.Dark2(np.linspace(0, 1, num_classes))\n",
        "\n",
        "# Menginisialisasi variabel iter_color untuk mengiterasi setiap warna pada array warna\n",
        "iter_color = iter(colors)\n",
        "\n",
        "# Membuat diagram batang horizontal berdasarkan jumlah text atau profil pelamar kerja yang termasuk ke dalam masing-masing kelas pada kolom \"Class\" pada dataset kamu\n",
        "df['Class'].value_counts().plot.barh(title=\"Reviews for each text (n, %)\",\n",
        "                                                 ylabel=\"Text\",\n",
        "                                                 color=colors,\n",
        "                                                 figsize=(9,9))\n",
        "\n",
        "# Menambahkan teks pada setiap batang diagram yang menunjukkan jumlah text atau profil pelamar kerja pada kelas tertentu\n",
        "for i, v in enumerate(df['Class'].value_counts()):\n",
        "    c = next(iter_color)\n",
        "    plt.text(v, i,\n",
        "             \" \"+str(v)+\", \"+str(round(v*100/df.shape[0],2))+\"%\",\n",
        "             color=c,\n",
        "             va='center',\n",
        "             fontweight='bold')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PCtrZKhYG8ty"
      },
      "source": [
        "Output yang dihasilkan oleh kode tersebut adalah diagram batang horizontal yang menunjukkan jumlah text atau profil pelamar kerja yang termasuk ke dalam masing-masing kelas pada kolom \"Class\" pada dataset. Setiap batang diagram memiliki teks di sebelah kanan batang yang menunjukkan jumlah text atau profil pelamar kerja pada kelas tertentu dalam bentuk angka dan persentase. Dari diagram tersebut, dapat melihat distribusi text atau profil pelamar kerja pada setiap kelas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMcna_C3eIJy",
        "outputId": "3046a558-7fa3-462a-8838-bb37e0d814ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting translate\n",
            "  Downloading translate-3.6.1-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from translate) (8.1.3)\n",
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            "Collecting libretranslatepy==2.1.1 (from translate)\n",
            "  Downloading libretranslatepy-2.1.1-py3-none-any.whl (3.2 kB)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (1.26.15)\n",
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            "Installing collected packages: libretranslatepy, translate\n",
            "Successfully installed libretranslatepy-2.1.1 translate-3.6.1\n"
          ]
        }
      ],
      "source": [
        "!pip install translate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bm1hDTdF5-hb"
      },
      "source": [
        "Kode ini akan mencetak contoh acak dari teks dalam kolom ‘Text’ yang memiliki nilai ‘Class’ sama dengan 0 dan terjemahannya dalam bahasa Inggris. Pastikan untuk mengganti df dengan nama variabel yang sesuai untuk dataset Anda dan menginstal pustaka yang diperlukan (translate dan termcolor) sebelum menjalankan kode tersebut."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuuvVfVtdM0z",
        "outputId": "3d8b9ad7-0a03-40b9-8394-ba16a7295669"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Original\n",
            "I really enjoy learning about the latest technology. Currently, I'm learning web development using express.js as Back-End and Next.js as Front-End. And I'm also learning about mobi...\n",
            "\n",
            "Translation\n",
            "Saya benar - benar menikmati belajar tentang teknologi terbaru. Saat ini, saya belajar pengembangan web menggunakan express.js sebagai Back - End dan Next.js sebagai Front - End. D...\n",
            "\n",
            "Original\n",
            "Hello my name is Dito, I am an iOS Mobile Developer (Native) Using Swift and also someone who is studying the back end of using Golang. Currently I'm hoping to get the opportunity ...\n",
            "\n",
            "Translation\n",
            "Halo nama saya Dito, saya seorang iOS Mobile Developer (Native) Menggunakan Swift dan juga seseorang yang sedang mempelajari back end menggunakan Golang. Saat ini saya berharap men...\n",
            "\n",
            "Original\n",
            "IÕam a UI/UX Designer in DANA Indonesia. Handling Payment Project : Send Money, Request Money, DANA Kaget, Split Bill, Cashout....\n",
            "\n",
            "Translation\n",
            "Saya adalah UI/UX Designer di DANA Indonesia. Penanganan Pembayaran Proyek : Kirim Uang, Minta Uang, DANA Kaget, Split Bill, Cashout....\n",
            "\n",
            "Original\n",
            "Experienced System Administrator with a demonstrated history of working in the online media industry. Skilled in Python (Programming Language), Linux System Administration, ELK, Do...\n",
            "\n",
            "Translation\n",
            "Administrator Sistem yang Berpengalaman dengan sejarah yang terbukti bekerja di industri media online. Terampil dalam Python (Bahasa Pemrograman), Linux System Administration, ELK,...\n"
          ]
        }
      ],
      "source": [
        "from translate import Translator\n",
        "from termcolor import colored\n",
        "\n",
        "translator= Translator(from_lang=\"en\", to_lang=\"id\")\n",
        "\n",
        "def print_rand_example(df, col_name, col_value, chars=180):\n",
        "    '''print a random review and its translation given a label\n",
        "    Args:\n",
        "      - df: input dataframe\n",
        "      - col_name: column to use as filter (e.g. Label)\n",
        "      - col_value: value of col_name to use as filter\n",
        "      - chars (optional, def:180) max number of characters to display\n",
        "    '''\n",
        "    original = df[df[col_name]==col_value].sample()[\"Text\"].values[0]\n",
        "    translation = translator.translate(original).replace(\"&#39;\",\"'\")\n",
        "    print(colored(\"\\nOriginal\", 'green', attrs=['bold','underline']))\n",
        "    print(original[0:chars] + \"...\")\n",
        "    print(colored(\"\\nTranslation\", 'red', attrs=['bold','underline']))\n",
        "    print(translation[0:chars] + \"...\")\n",
        "\n",
        "# contoh penggunaan fungsi print_rand_example\n",
        "print_rand_example(df, 'Class', 0)\n",
        "print_rand_example(df, 'Class', 1)\n",
        "print_rand_example(df, 'Class', 2)\n",
        "print_rand_example(df, 'Class', 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdBJ9wCl6Iio"
      },
      "source": [
        "Kode ini akan membagi dataset Anda menjadi data latih dan data uji dengan proporsi 75:25. Kolom ‘Text’ akan digunakan sebagai fitur dan kolom ‘Class’ akan digunakan sebagai label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5t1BBAfet61",
        "outputId": "4b52c398-ada3-4c2e-cd01-86087a81a41f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah data pada data training: 164\n",
            "Jumlah data pada data testing: 55\n",
            "Jumlah label pada data training: 164\n",
            "Jumlah label pada data testing: 55\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "num_classes = len(df[\"Class\"].value_counts())\n",
        "y = tf.keras.utils.to_categorical(df[\"Class\"].values, num_classes=num_classes)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df['Text'], y, test_size=0.25)\n",
        "\n",
        "print(\"Jumlah data pada data training:\", len(x_train))\n",
        "print(\"Jumlah data pada data testing:\", len(x_test))\n",
        "print(\"Jumlah label pada data training:\", len(y_train))\n",
        "print(\"Jumlah label pada data testing:\", len(y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l50Z-vUx6wuj"
      },
      "source": [
        "Yang ingin saya capai adalah mengubah teks menjadi vektor berdimensi tinggi yang menangkap semantik tingkat kalimat. Oleh karena itu, saya melanjutkan dengan memuat lapisan preprocessor dan encoder dari titik akhir yang disediakan oleh TensorFlow Hub, dan menentukan fungsi sederhana untuk mendapatkan penyematan dari teks masukan.\n",
        "\n",
        "Hal yang penting adalah bahwa seseorang dapat memilih untuk mengimpor model preferensi apa pun tergantung pada tugas dan bahasa masukan.\n",
        "\n",
        "Karena model ini didasarkan pada arsitektur transformator BERT, model ini akan menghasilkan pooled_output (penyematan keluaran dari seluruh urutan) bentuk [ukuran batch, 768]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tpcqDb4Qe6ur",
        "outputId": "8731b81b-7a94-4a41-b818-3685c59e53a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[-0.22663741 -0.14343746 -0.32012492 ... -0.04661513 -0.06341164\n",
            "   0.10188575]\n",
            " [-0.04950407  0.00416472  0.09135172 ... -0.3943716  -0.13816215\n",
            "  -0.02260007]\n",
            " [-0.37836957 -0.18587808 -0.27618733 ... -0.14031428  0.08200935\n",
            "  -0.372581  ]\n",
            " ...\n",
            " [ 0.17531103 -0.21574299 -0.06876095 ... -0.0851216   0.1194027\n",
            "   0.11596426]\n",
            " [ 0.07690524 -0.23456529 -0.3573915  ...  0.18296139 -0.2959927\n",
            "   0.14599116]\n",
            " [-0.25802502 -0.28717923 -0.24516018 ... -0.34850487 -0.28197345\n",
            "  -0.33899269]], shape=(219, 768), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text\n",
        "\n",
        "preprocessor = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-preprocess/2\", trainable=False)\n",
        "encoder = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-base/1\", trainable=False)\n",
        "model = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-base/1\")\n",
        "\n",
        "def get_embeddings(sentences):\n",
        "  '''return BERT-like embeddings of input text\n",
        "  Args:\n",
        "    - sentences: list of strings\n",
        "  Output:\n",
        "    - BERT-like embeddings: tf.Tensor of shape=(len(sentences), 768)\n",
        "  '''\n",
        "  preprocessed_text = preprocessor(sentences)\n",
        "  return encoder(preprocessed_text)['pooled_output']\n",
        "\n",
        "embeddings = get_embeddings(df[\"Text\"].tolist())\n",
        "print(embeddings)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LNDDgkER604",
        "outputId": "bf7bf0e7-1ce7-4731-bcf0-e251b3ad29fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m63.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.29.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rUu782sT69z7",
        "outputId": "7fc5415d-c8b3-4918-cda1-e406cd0c5db9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In layer  word_embeddings/embeddings:0\n",
            "In layer  position_embedding/embeddings:0\n",
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            "In layer  transformer/layer_6/self_attention/key/bias:0\n",
            "In layer  transformer/layer_6/self_attention/value/kernel:0\n",
            "In layer  transformer/layer_6/self_attention/value/bias:0\n",
            "In layer  transformer/layer_6/self_attention/attention_output/kernel:0\n",
            "In layer  transformer/layer_6/self_attention/attention_output/bias:0\n",
            "In layer  transformer/layer_6/self_attention_layer_norm/gamma:0\n",
            "In layer  transformer/layer_6/self_attention_layer_norm/beta:0\n",
            "In layer  transformer/layer_6/intermediate/kernel:0\n",
            "In layer  transformer/layer_6/intermediate/bias:0\n",
            "In layer  transformer/layer_6/output/kernel:0\n",
            "In layer  transformer/layer_6/output/bias:0\n",
            "In layer  transformer/layer_6/output_layer_norm/gamma:0\n",
            "In layer  transformer/layer_6/output_layer_norm/beta:0\n",
            "In layer  transformer/layer_7/self_attention/query/kernel:0\n",
            "In layer  transformer/layer_7/self_attention/query/bias:0\n",
            "In layer  transformer/layer_7/self_attention/key/kernel:0\n",
            "In layer  transformer/layer_7/self_attention/key/bias:0\n",
            "In layer  transformer/layer_7/self_attention/value/kernel:0\n",
            "In layer  transformer/layer_7/self_attention/value/bias:0\n",
            "In layer  transformer/layer_7/self_attention/attention_output/kernel:0\n",
            "In layer  transformer/layer_7/self_attention/attention_output/bias:0\n",
            "In layer  transformer/layer_7/self_attention_layer_norm/gamma:0\n",
            "In layer  transformer/layer_7/self_attention_layer_norm/beta:0\n",
            "In layer  transformer/layer_7/intermediate/kernel:0\n",
            "In layer  transformer/layer_7/intermediate/bias:0\n",
            "In layer  transformer/layer_7/output/kernel:0\n",
            "In layer  transformer/layer_7/output/bias:0\n",
            "In layer  transformer/layer_7/output_layer_norm/gamma:0\n",
            "In layer  transformer/layer_7/output_layer_norm/beta:0\n",
            "In layer  transformer/layer_8/self_attention/query/kernel:0\n",
            "In layer  transformer/layer_8/self_attention/query/bias:0\n",
            "In layer  transformer/layer_8/self_attention/key/kernel:0\n",
            "In layer  transformer/layer_8/self_attention/key/bias:0\n",
            "In layer  transformer/layer_8/self_attention/value/kernel:0\n",
            "In layer  transformer/layer_8/self_attention/value/bias:0\n",
            "In layer  transformer/layer_8/self_attention/attention_output/kernel:0\n",
            "In layer  transformer/layer_8/self_attention/attention_output/bias:0\n",
            "In layer  transformer/layer_8/self_attention_layer_norm/gamma:0\n",
            "In layer  transformer/layer_8/self_attention_layer_norm/beta:0\n",
            "In layer  transformer/layer_8/intermediate/kernel:0\n",
            "In layer  transformer/layer_8/intermediate/bias:0\n",
            "In layer  transformer/layer_8/output/kernel:0\n",
            "In layer  transformer/layer_8/output/bias:0\n",
            "In layer  transformer/layer_8/output_layer_norm/gamma:0\n",
            "In layer  transformer/layer_8/output_layer_norm/beta:0\n",
            "In layer  transformer/layer_9/self_attention/query/kernel:0\n",
            "In layer  transformer/layer_9/self_attention/query/bias:0\n",
            "In layer  transformer/layer_9/self_attention/key/kernel:0\n",
            "In layer  transformer/layer_9/self_attention/key/bias:0\n",
            "In layer  transformer/layer_9/self_attention/value/kernel:0\n",
            "In layer  transformer/layer_9/self_attention/value/bias:0\n",
            "In layer  transformer/layer_9/self_attention/attention_output/kernel:0\n",
            "In layer  transformer/layer_9/self_attention/attention_output/bias:0\n",
            "In layer  transformer/layer_9/self_attention_layer_norm/gamma:0\n",
            "In layer  transformer/layer_9/self_attention_layer_norm/beta:0\n",
            "In layer  transformer/layer_9/intermediate/kernel:0\n",
            "In layer  transformer/layer_9/intermediate/bias:0\n",
            "In layer  transformer/layer_9/output/kernel:0\n",
            "In layer  transformer/layer_9/output/bias:0\n",
            "In layer  transformer/layer_9/output_layer_norm/gamma:0\n",
            "In layer  transformer/layer_9/output_layer_norm/beta:0\n",
            "In layer  transformer/layer_10/self_attention/query/kernel:0\n",
            "In layer  transformer/layer_10/self_attention/query/bias:0\n",
            "In layer  transformer/layer_10/self_attention/key/kernel:0\n",
            "In layer  transformer/layer_10/self_attention/key/bias:0\n",
            "In layer  transformer/layer_10/self_attention/value/kernel:0\n",
            "In layer  transformer/layer_10/self_attention/value/bias:0\n",
            "In layer  transformer/layer_10/self_attention/attention_output/kernel:0\n",
            "In layer  transformer/layer_10/self_attention/attention_output/bias:0\n",
            "In layer  transformer/layer_10/self_attention_layer_norm/gamma:0\n",
            "In layer  transformer/layer_10/self_attention_layer_norm/beta:0\n",
            "In layer  transformer/layer_10/intermediate/kernel:0\n",
            "In layer  transformer/layer_10/intermediate/bias:0\n",
            "In layer  transformer/layer_10/output/kernel:0\n",
            "In layer  transformer/layer_10/output/bias:0\n",
            "In layer  transformer/layer_10/output_layer_norm/gamma:0\n",
            "In layer  transformer/layer_10/output_layer_norm/beta:0\n",
            "In layer  transformer/layer_11/self_attention/query/kernel:0\n",
            "In layer  transformer/layer_11/self_attention/query/bias:0\n",
            "In layer  transformer/layer_11/self_attention/key/kernel:0\n",
            "In layer  transformer/layer_11/self_attention/key/bias:0\n",
            "In layer  transformer/layer_11/self_attention/value/kernel:0\n",
            "In layer  transformer/layer_11/self_attention/value/bias:0\n",
            "In layer  transformer/layer_11/self_attention/attention_output/kernel:0\n",
            "In layer  transformer/layer_11/self_attention/attention_output/bias:0\n",
            "In layer  transformer/layer_11/self_attention_layer_norm/gamma:0\n",
            "In layer  transformer/layer_11/self_attention_layer_norm/beta:0\n",
            "In layer  transformer/layer_11/intermediate/kernel:0\n",
            "In layer  transformer/layer_11/intermediate/bias:0\n",
            "In layer  transformer/layer_11/output/kernel:0\n",
            "In layer  transformer/layer_11/output/bias:0\n",
            "In layer  transformer/layer_11/output_layer_norm/gamma:0\n",
            "In layer  transformer/layer_11/output_layer_norm/beta:0\n",
            "In layer  pooler_transform/kernel:0\n",
            "In layer  pooler_transform/bias:0\n",
            "In layer  Variable:0\n"
          ]
        }
      ],
      "source": [
        "for i in range(len(encoder.weights)):\n",
        "    print(\"In layer \",encoder.weights[i].name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9wldKmIHj8T"
      },
      "source": [
        "Output yang diberikan adalah representasi vektor teks untuk setiap kalimat dalam dataset, dengan bentuk (119, 768), di mana 119 adalah jumlah kalimat dalam dataset, dan 768 adalah dimensi dari setiap vektor representasi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLkS4HGKIScS"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        },
        "id": "l2Ad6ZwufX95",
        "outputId": "06164e09-4186-4e97-e8c1-8ba9c15585e2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import string\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def clean_text(text):\n",
        "    \"\"\"Membersihkan teks dengan menghapus stopwords dan tanda baca, dan mengonversi ke lowercase.\"\"\"\n",
        "    # Menghapus tanda baca\n",
        "    text = re.sub(r'[^\\w\\s]', '', text)\n",
        "    # Mengonversi ke lowercase\n",
        "    text = text.lower()\n",
        "    # Menghapus stopwords\n",
        "    stopwords_list = set(stopwords.words('english'))\n",
        "    text = ' '.join(word for word in text.split() if word not in stopwords_list)\n",
        "    return text\n",
        "\n",
        "def plot_similarity(features, labels):\n",
        "  \"\"\"Plot a similarity matrix of the embeddings.\"\"\"\n",
        "  cos_sim = cosine_similarity(features)\n",
        "  fig = plt.figure(figsize=(10,8))\n",
        "  sns.set(font_scale=1.2)\n",
        "  cbar_kws=dict(use_gridspec=False, location=\"left\")\n",
        "  g = sns.heatmap(\n",
        "      cos_sim, xticklabels=labels, yticklabels=labels,\n",
        "      vmin=0, vmax=1, annot=True, cmap=\"Blues\",\n",
        "      cbar_kws=cbar_kws)\n",
        "  g.tick_params(labelright=True, labelleft=False)\n",
        "  g.set_yticklabels(labels, rotation=0)\n",
        "  g.set_title(\"Semantic Textual Similarity\")\n",
        "\n",
        "def preprocess_text(text):\n",
        "  \"\"\"Preprocess a piece of text by removing stopwords, punctuation, and converting to lowercase.\"\"\"\n",
        "  stopwords_set = set(stopwords.words('english'))\n",
        "  text = ' '.join([word for word in text.split() if word.lower() not in stopwords_set])\n",
        "  text = ''.join([char for char in text if char not in string.punctuation])\n",
        "  text = text.lower()\n",
        "  return text\n",
        "\n",
        "# Mengubah teks di dalam kolom 'Text' menjadi embeddings dan memplot matriks kesamaannya\n",
        "reviews = [\"Laravel, JS Newbie, Vue and Flutter\",\n",
        "           \"Android developer at DANA Indonesia. Informatics graduates of Brawijaya University. Passionate in mobile app development.\",\n",
        "           \"A college student who love technology and create projects about web and multi-platform apps.\",\n",
        "           \"I like a watermelon\"]\n",
        "\n",
        "# Preprocess the reviews\n",
        "reviews = [preprocess_text(review) for review in reviews]\n",
        "\n",
        "# Plot similarity matrix\n",
        "plot_similarity(get_embeddings(reviews), reviews)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52QhexRPIWHR"
      },
      "source": [
        "Output menunjukkan matriks hotmap yang mewakili skor kesamaan berpasangan antara teks input. Skor kesamaan dihitung menggunakan kesamaan kosinus antara penyematan teks masukan, yang diperoleh dengan menggunakan model Word2Vec dari perpustakaan gensim.\n",
        "\n",
        "Matriks bersifat simetris, dimana setiap elemen dalam matriks merepresentasikan skor kesamaan antara dua teks. Elemen diagonal mewakili kemiripan setiap teks dengan dirinya sendiri, yang selalu 1. Semakin gelap warna sel, semakin tinggi skor kesamaan antara teks yang bersesuaian.\n",
        "\n",
        "Dalam contoh khusus ini, teks inputnya adalah:\n",
        "\n",
        "\"Laravel, Pemula JS, Vue, dan Flutter\"\n",
        "\"Pengembang Android di DANA Indonesia. Lulusan Informatika Universitas Brawijaya. Bergairah dalam pengembangan aplikasi seluler.\"\n",
        "\"Seorang mahasiswa yang menyukai teknologi dan membuat proyek tentang aplikasi web dan multi-platform.\"\n",
        "\n",
        "Dari heatmap, kita dapat melihat bahwa teks pertama dan ketiga memiliki skor kesamaan terendah sekitar 0,4, yang menunjukkan bahwa mereka kurang mirip dibandingkan dengan pasangan lainnya. Pasangan lainnya memiliki skor kesamaan mulai dari 0,6 hingga 0,8, menunjukkan tingkat kesamaan yang sedang hingga tinggi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dclu8cnJJzmQ"
      },
      "source": [
        "Proses perbandingan dengan TFIDF\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "riDux8tCJdzy",
        "outputId": "56c4f948-4393-4d3e-d277-bade3375717c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from nltk.corpus import stopwords\n",
        "import string\n",
        "import re\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "\n",
        "def clean_text(text):\n",
        "    \"\"\"Membersihkan teks dengan menghapus stopwords dan tanda baca, dan mengonversi ke lowercase.\"\"\"\n",
        "    # Menghapus tanda baca\n",
        "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
        "    # Mengonversi ke lowercase\n",
        "    text = text.lower()\n",
        "    # Menghapus stopwords\n",
        "    stopwords_list = set(stopwords.words('english'))\n",
        "    text = ' '.join(word for word in text.split() if word not in stopwords_list)\n",
        "    return text\n",
        "\n",
        "def get_tfidf_vectors(texts):\n",
        "    # Membersihkan teks\n",
        "    cleaned_texts = [clean_text(text) for text in texts]\n",
        "    # Mengubah teks menjadi vektor fitur menggunakan TF-IDF setelah membersihkan teks\n",
        "    vectorizer = TfidfVectorizer()\n",
        "    tfidf_vectors = vectorizer.fit_transform(cleaned_texts)\n",
        "    return tfidf_vectors.toarray()\n",
        "\n",
        "def plot_similarity(features, labels):\n",
        "    \"\"\"Plot a similarity matrix of the embeddings.\"\"\"\n",
        "    cos_sim = cosine_similarity(features)\n",
        "    fig = plt.figure(figsize=(10,8))\n",
        "    sns.set(font_scale=1.2)\n",
        "    cbar_kws=dict(use_gridspec=False, location=\"left\")\n",
        "    g = sns.heatmap(\n",
        "        cos_sim, xticklabels=labels, yticklabels=labels,\n",
        "        vmin=0, vmax=1, annot=True, cmap=\"Blues\",\n",
        "        cbar_kws=cbar_kws)\n",
        "    g.tick_params(labelright=True, labelleft=False)\n",
        "    g.set_yticklabels(labels, rotation=0)\n",
        "    g.set_title(\"Semantic Textual Similarity\")\n",
        "\n",
        "# Membuat DataFrame contoh untuk data Anda\n",
        "data = {\n",
        "  'Text': [\"Laravel, JS Newbie, Vue and Flutter\",\n",
        "           \"Android developer at DANA Indonesia. Informatics graduates of Brawijaya University. Passionate in mobile app development.\",\n",
        "           \"A college student who love technology and create projects about web and multi-platform apps.\",\n",
        "           \"I love watermelon\"]\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Mengubah teks di dalam kolom 'Text' menjadi vektor fitur menggunakan TF-IDF setelah membersihkan teks\n",
        "tfidf_vectors = get_tfidf_vectors(df['Text'])\n",
        "\n",
        "# Memplot matriks kesamaan\n",
        "plot_similarity(tfidf_vectors, df['Text'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVamEmQoh6g4"
      },
      "source": [
        "Dari perbandingan output tersebut, kita dapat menyimpulkan bahwa Word2Vec menghasilkan embedding teks yang lebih baik dalam memperhitungkan kesamaan semantik antara dokumen. Hal ini terlihat dari matriks kesamaan yang dihasilkan oleh Word2Vec, yang menunjukkan pola yang lebih jelas dan konsisten dalam memperlihatkan kesamaan antara dokumen. Di sisi lain, matriks kesamaan yang dihasilkan oleh TF-IDF lebih difokuskan pada kata-kata kunci dalam dokumen, sehingga meskipun masih dapat menunjukkan kesamaan antara dokumen, tetapi tidak sejelas dan sekuat seperti Word2Vec.\n",
        "\n",
        "Oleh karena itu, jika tujuan utama adalah untuk mengekstrak makna dari teks dan mengukur kesamaan semantik antara dokumen, maka Word2Vec lebih disarankan. Namun, jika tujuan utama adalah untuk mengekstrak kata-kata kunci atau memperhitungkan kesamaan berdasarkan kemunculan kata-kata dalam dokumen, maka TF-IDF lebih disarankan."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "76TS4fEKM3YW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v7tdL4emr2xa"
      },
      "outputs": [],
      "source": [
        "from keras import backend as K\n",
        "\n",
        "def balanced_recall(y_true, y_pred):\n",
        "    \"\"\"This function calculates the balanced recall metric\n",
        "    recall = TP / (TP + FN)\n",
        "    \"\"\"\n",
        "    recall_by_class = 0\n",
        "    # iterate over each predicted class to get class-specific metric\n",
        "    for i in range(y_pred.shape[1]):\n",
        "        y_pred_class = y_pred[:, i]\n",
        "        y_true_class = y_true[:, i]\n",
        "        true_positives = K.sum(K.round(K.clip(y_true_class * y_pred_class, 0, 1)))\n",
        "        possible_positives = K.sum(K.round(K.clip(y_true_class, 0, 1)))\n",
        "        recall = true_positives / (possible_positives + K.epsilon())\n",
        "        recall_by_class = recall_by_class + recall\n",
        "    return recall_by_class / y_pred.shape[1]\n",
        "\n",
        "def balanced_precision(y_true, y_pred):\n",
        "    \"\"\"This function calculates the balanced precision metric\n",
        "    precision = TP / (TP + FP)\n",
        "    \"\"\"\n",
        "    precision_by_class = 0\n",
        "    # iterate over each predicted class to get class-specific metric\n",
        "    for i in range(y_pred.shape[1]):\n",
        "        y_pred_class = y_pred[:, i]\n",
        "        y_true_class = y_true[:, i]\n",
        "        true_positives = K.sum(K.round(K.clip(y_true_class * y_pred_class, 0, 1)))\n",
        "        predicted_positives = K.sum(K.round(K.clip(y_pred_class, 0, 1)))\n",
        "        precision = true_positives / (predicted_positives + K.epsilon())\n",
        "        precision_by_class = precision_by_class + precision\n",
        "    # return average balanced metric for each class\n",
        "    return precision_by_class / y_pred.shape[1]\n",
        "\n",
        "def balanced_f1_score(y_true, y_pred):\n",
        "    \"\"\"This function calculates the F1 score metric\"\"\"\n",
        "    precision = balanced_precision(y_true, y_pred)\n",
        "    recall = balanced_recall(y_true, y_pred)\n",
        "    return 2 * ((precision * recall) / (precision + recall + K.epsilon()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehPlZsKC_1Cg"
      },
      "source": [
        "implementasi dari tiga metrik evaluasi klasifikasi, yaitu balanced recall, balanced precision, dan balanced F1 score.\n",
        "\n",
        "Balanced recall mengukur seberapa baik model dalam mengenali kelas positif dengan memperhitungkan false negative rate. Balanced precision mengukur seberapa baik model dalam memprediksi kelas positif dengan memperhitungkan false positive rate. Balanced F1 score merupakan gabungan dari balanced recall dan balanced precision, yang menghasilkan nilai yang lebih stabil dan seimbang.\n",
        "\n",
        "Dalam implementasinya, ketiga metrik tersebut memerlukan argumen y_true dan y_pred, yaitu matriks label aktual dan matriks label prediksi yang dihasilkan oleh model. Kemudian, dalam tiap metrik, dilakukan iterasi pada tiap kelas untuk menghitung masing-masing metrik.\n",
        "\n",
        "Pada balanced recall, setiap kelas diproses untuk menghitung recall, yaitu true positive rate, yang dihitung dengan membagi jumlah true positive dengan jumlah possible positive. Pada balanced precision, setiap kelas diproses untuk menghitung precision, yaitu true positive rate, yang dihitung dengan membagi jumlah true positive dengan jumlah predicted positive.\n",
        "\n",
        "Pada balanced F1 score, metrik dihitung dengan menggunakan nilai precision dan recall yang telah dihitung sebelumnya untuk setiap kelas, dan kemudian dihitung nilai F1 score-nya menggunakan formula yang telah ditentukan."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ugRDK5ZJkw22"
      },
      "outputs": [],
      "source": [
        "i = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
        "x = preprocessor(i)\n",
        "x = encoder(x)\n",
        "x = tf.keras.layers.Dropout(0.2, name=\"dropout\")(x['pooled_output'])\n",
        "x = tf.keras.layers.Dense(num_classes, activation='softmax', name=\"output\")(x)\n",
        "\n",
        "model = tf.keras.Model(i, x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjgDNg7iAyq9"
      },
      "source": [
        "Implementasi arsitektur model jaringan saraf tiruan (neural network) yang dibangun menggunakan TensorFlow. Model ini terdiri dari beberapa lapisan, yaitu:\n",
        "\n",
        "Input layer: i = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text'). Ini adalah layer input yang digunakan untuk menerima input berupa data teks. shape=() menandakan bahwa input dapat memiliki dimensi apapun. dtype=tf.string menandakan bahwa tipe data input adalah string. name='text' memberikan nama untuk layer ini.\n",
        "\n",
        "Preprocessor layer: x = preprocessor(i). Layer ini digunakan untuk melakukan preprocessing pada input teks. Fungsi preprocessor mungkin telah didefinisikan sebelumnya di kode programmu.\n",
        "\n",
        "Encoder layer: x = encoder(x). Layer ini digunakan untuk melakukan encoding pada input teks yang telah diproses oleh preprocessor. encoder mungkin juga telah didefinisikan sebelumnya di kode programmu.\n",
        "\n",
        "Dropout layer: x = tf.keras.layers.Dropout(0.2, name=\"dropout\")(x['pooled_output']). Layer ini digunakan untuk mencegah overfitting pada model dengan menonaktifkan sebagian output dari layer sebelumnya (encoder). 0.2 menandakan proporsi output yang dinonaktifkan (20%). name=\"dropout\" memberikan nama untuk layer ini.\n",
        "\n",
        "Output layer: x = tf.keras.layers.Dense(num_classes, activation='softmax', name=\"output\")(x). Ini adalah layer output yang digunakan untuk menghasilkan output berupa probabilitas kelas. num_classes menandakan jumlah kelas pada datasetmu. activation='softmax' digunakan karena output dari model ini adalah probabilitas kelas. name=\"output\" memberikan nama untuk layer ini.\n",
        "\n",
        "Model: model = tf.keras.Model(i, x). Ini adalah model akhir yang digunakan untuk training dan inference. Model ini dibangun dengan menggunakan input layer (i) dan output layer (x) yang telah dibuat sebelumnya."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGf6nUXZshaX",
        "outputId": "61fbb331-b9f9-43ea-da8d-d6c04b83f853"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "6/6 [==============================] - 115s 15s/step - loss: 1.3566 - accuracy: 0.3293 - balanced_recall: 0.0236 - balanced_precision: 0.1458 - balanced_f1_score: 0.0407 - val_loss: 1.2869 - val_accuracy: 0.3455 - val_balanced_recall: 0.0000e+00 - val_balanced_precision: 0.0000e+00 - val_balanced_f1_score: 0.0000e+00\n",
            "Epoch 2/20\n",
            "6/6 [==============================] - 75s 13s/step - loss: 1.2588 - accuracy: 0.4329 - balanced_recall: 0.0305 - balanced_precision: 0.2083 - balanced_f1_score: 0.0520 - val_loss: 1.2575 - val_accuracy: 0.3455 - val_balanced_recall: 0.0000e+00 - val_balanced_precision: 0.0000e+00 - val_balanced_f1_score: 0.0000e+00\n",
            "Epoch 3/20\n",
            "6/6 [==============================] - 74s 13s/step - loss: 1.1694 - accuracy: 0.5061 - balanced_recall: 0.1309 - balanced_precision: 0.5486 - balanced_f1_score: 0.1922 - val_loss: 1.1857 - val_accuracy: 0.5455 - val_balanced_recall: 0.0139 - val_balanced_precision: 0.1250 - val_balanced_f1_score: 0.0250\n",
            "Epoch 4/20\n",
            "6/6 [==============================] - 79s 14s/step - loss: 1.1019 - accuracy: 0.5793 - balanced_recall: 0.0927 - balanced_precision: 0.4062 - balanced_f1_score: 0.1468 - val_loss: 1.1552 - val_accuracy: 0.6545 - val_balanced_recall: 0.0417 - val_balanced_precision: 0.1875 - val_balanced_f1_score: 0.0635\n",
            "Epoch 5/20\n",
            "6/6 [==============================] - 72s 13s/step - loss: 1.0395 - accuracy: 0.6463 - balanced_recall: 0.1061 - balanced_precision: 0.4792 - balanced_f1_score: 0.1723 - val_loss: 1.1305 - val_accuracy: 0.6545 - val_balanced_recall: 0.0667 - val_balanced_precision: 0.3125 - val_balanced_f1_score: 0.1074\n",
            "Epoch 6/20\n",
            "6/6 [==============================] - 74s 13s/step - loss: 1.0001 - accuracy: 0.6951 - balanced_recall: 0.2044 - balanced_precision: 0.6771 - balanced_f1_score: 0.3069 - val_loss: 1.1116 - val_accuracy: 0.6000 - val_balanced_recall: 0.0667 - val_balanced_precision: 0.2708 - val_balanced_f1_score: 0.1025\n",
            "Epoch 7/20\n",
            "6/6 [==============================] - 75s 13s/step - loss: 0.9509 - accuracy: 0.7134 - balanced_recall: 0.2682 - balanced_precision: 0.6896 - balanced_f1_score: 0.3763 - val_loss: 1.0921 - val_accuracy: 0.6000 - val_balanced_recall: 0.1118 - val_balanced_precision: 0.3958 - val_balanced_f1_score: 0.1737\n",
            "Epoch 8/20\n",
            "6/6 [==============================] - 76s 13s/step - loss: 0.9068 - accuracy: 0.7500 - balanced_recall: 0.2928 - balanced_precision: 0.7867 - balanced_f1_score: 0.4240 - val_loss: 1.0718 - val_accuracy: 0.6182 - val_balanced_recall: 0.1056 - val_balanced_precision: 0.2917 - val_balanced_f1_score: 0.1515\n",
            "Epoch 9/20\n",
            "6/6 [==============================] - 74s 13s/step - loss: 0.8637 - accuracy: 0.7317 - balanced_recall: 0.4074 - balanced_precision: 0.8438 - balanced_f1_score: 0.5449 - val_loss: 1.0503 - val_accuracy: 0.6000 - val_balanced_recall: 0.1194 - val_balanced_precision: 0.2917 - val_balanced_f1_score: 0.1686\n",
            "Epoch 10/20\n",
            "6/6 [==============================] - 76s 13s/step - loss: 0.8333 - accuracy: 0.7378 - balanced_recall: 0.3863 - balanced_precision: 0.8444 - balanced_f1_score: 0.5280 - val_loss: 1.0114 - val_accuracy: 0.6364 - val_balanced_recall: 0.1882 - val_balanced_precision: 0.4917 - val_balanced_f1_score: 0.2722\n",
            "Epoch 11/20\n",
            "6/6 [==============================] - 74s 13s/step - loss: 0.8035 - accuracy: 0.7988 - balanced_recall: 0.3675 - balanced_precision: 0.7381 - balanced_f1_score: 0.4876 - val_loss: 0.9894 - val_accuracy: 0.6727 - val_balanced_recall: 0.2549 - val_balanced_precision: 0.7604 - val_balanced_f1_score: 0.3816\n",
            "Epoch 12/20\n",
            "6/6 [==============================] - 75s 13s/step - loss: 0.7507 - accuracy: 0.7927 - balanced_recall: 0.5127 - balanced_precision: 0.8634 - balanced_f1_score: 0.6305 - val_loss: 0.9698 - val_accuracy: 0.6545 - val_balanced_recall: 0.2924 - val_balanced_precision: 0.8958 - val_balanced_f1_score: 0.4394\n",
            "Epoch 13/20\n",
            "6/6 [==============================] - 76s 12s/step - loss: 0.7419 - accuracy: 0.8171 - balanced_recall: 0.5549 - balanced_precision: 0.8818 - balanced_f1_score: 0.6791 - val_loss: 0.9426 - val_accuracy: 0.7273 - val_balanced_recall: 0.2674 - val_balanced_precision: 0.7292 - val_balanced_f1_score: 0.3907\n",
            "Epoch 14/20\n",
            "6/6 [==============================] - 75s 13s/step - loss: 0.6964 - accuracy: 0.8354 - balanced_recall: 0.5434 - balanced_precision: 0.8922 - balanced_f1_score: 0.6739 - val_loss: 0.9183 - val_accuracy: 0.7455 - val_balanced_recall: 0.2951 - val_balanced_precision: 0.7417 - val_balanced_f1_score: 0.4214\n",
            "Epoch 15/20\n",
            "6/6 [==============================] - 75s 13s/step - loss: 0.6812 - accuracy: 0.8232 - balanced_recall: 0.4786 - balanced_precision: 0.8597 - balanced_f1_score: 0.6113 - val_loss: 0.8995 - val_accuracy: 0.8000 - val_balanced_recall: 0.3354 - val_balanced_precision: 0.7619 - val_balanced_f1_score: 0.4654\n",
            "Epoch 16/20\n",
            "6/6 [==============================] - 77s 13s/step - loss: 0.6722 - accuracy: 0.8415 - balanced_recall: 0.5910 - balanced_precision: 0.8798 - balanced_f1_score: 0.7052 - val_loss: 0.8847 - val_accuracy: 0.8000 - val_balanced_recall: 0.3694 - val_balanced_precision: 0.6882 - val_balanced_f1_score: 0.4807\n",
            "Epoch 17/20\n",
            "6/6 [==============================] - 75s 13s/step - loss: 0.6392 - accuracy: 0.8476 - balanced_recall: 0.5778 - balanced_precision: 0.9167 - balanced_f1_score: 0.7070 - val_loss: 0.8642 - val_accuracy: 0.7636 - val_balanced_recall: 0.3944 - val_balanced_precision: 0.7351 - val_balanced_f1_score: 0.5133\n",
            "Epoch 18/20\n",
            "6/6 [==============================] - 75s 13s/step - loss: 0.6014 - accuracy: 0.8963 - balanced_recall: 0.6234 - balanced_precision: 0.9009 - balanced_f1_score: 0.7329 - val_loss: 0.8426 - val_accuracy: 0.7455 - val_balanced_recall: 0.4118 - val_balanced_precision: 0.8862 - val_balanced_f1_score: 0.5622\n",
            "Epoch 19/20\n",
            "6/6 [==============================] - 76s 13s/step - loss: 0.5963 - accuracy: 0.8537 - balanced_recall: 0.6350 - balanced_precision: 0.9115 - balanced_f1_score: 0.7471 - val_loss: 0.8332 - val_accuracy: 0.7455 - val_balanced_recall: 0.4104 - val_balanced_precision: 0.8862 - val_balanced_f1_score: 0.5609\n",
            "Epoch 20/20\n",
            "6/6 [==============================] - 76s 13s/step - loss: 0.5754 - accuracy: 0.8780 - balanced_recall: 0.7608 - balanced_precision: 0.9402 - balanced_f1_score: 0.8388 - val_loss: 0.8173 - val_accuracy: 0.7636 - val_balanced_recall: 0.4507 - val_balanced_precision: 0.9122 - val_balanced_f1_score: 0.6028\n"
          ]
        }
      ],
      "source": [
        "n_epochs = 20\n",
        "\n",
        "METRICS = [\n",
        "      tf.keras.metrics.CategoricalAccuracy(name=\"accuracy\"),\n",
        "      balanced_recall,\n",
        "      balanced_precision,\n",
        "      balanced_f1_score\n",
        "]\n",
        "\n",
        "earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\",\n",
        "                                                      patience = 3,\n",
        "                                                      restore_best_weights = True)\n",
        "\n",
        "model.compile(optimizer = \"adam\",\n",
        "              loss = \"categorical_crossentropy\",\n",
        "              metrics = METRICS\n",
        ")\n",
        "\n",
        "model_fit = model.fit(x_train,\n",
        "                      y_train,\n",
        "                      epochs = n_epochs,\n",
        "                      validation_data = (x_test, y_test),\n",
        "                      callbacks = [earlystop_callback])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dRH2C8PS8I1i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd1fc051-a41a-4938-a98a-ee241b3b0ac9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 337). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "model.compile(optimizer = \"adam\",\n",
        "              loss = \"categorical_crossentropy\",\n",
        ")\n",
        "model.save('/content/drive/MyDrive/WEB SOSES/BERT_TF1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q_LDvTNzEYXZ"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "model = keras.models.load_model('/content/drive/MyDrive/WEB SOSES/BERT_TF1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8N7EOnVBIbt5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80450fab-2133-47e1-9ece-04b39b961b2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " text (InputLayer)              [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)       {'input_word_ids':   0           ['text[0][0]']                   \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_mask': (Non                                               \n",
            "                                e, 128),                                                          \n",
            "                                 'input_type_ids':                                                \n",
            "                                (None, 128)}                                                      \n",
            "                                                                                                  \n",
            " keras_layer_1 (KerasLayer)     {'default': (None,   470926849   ['keras_layer[0][0]',            \n",
            "                                768),                             'keras_layer[0][1]',            \n",
            "                                 'pooled_output': (               'keras_layer[0][2]']            \n",
            "                                None, 768),                                                       \n",
            "                                 'sequence_output':                                               \n",
            "                                 (None, 128, 768)}                                                \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 768)          0           ['keras_layer_1[0][1]']          \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 4)            3076        ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 470,929,925\n",
            "Trainable params: 3,076\n",
            "Non-trainable params: 470,926,849\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRIEW9QPDCQM"
      },
      "source": [
        "n_epochs = 20 menentukan jumlah epoch atau iterasi yang akan dilakukan selama pelatihan. Epoch adalah satu iterasi melalui seluruh dataset pelatihan. Dalam kasus ini, model akan melihat seluruh dataset pelatihan sebanyak 20 kali.\n",
        "\n",
        "METRICS adalah daftar metrik evaluasi kinerja model yang akan digunakan selama pelatihan. Di sini, kita memiliki metrik akurasi kategorikal, recall seimbang, presisi seimbang, dan skor f1 seimbang.\n",
        "\n",
        "earlystop_callback adalah objek dari kelas EarlyStopping yang akan menghentikan pelatihan jika tidak ada perbaikan yang terlihat dalam patience epoch terakhir. Jadi, jika setelah tiga epoch berturut-turut tidak ada peningkatan pada val_loss (kerugian validasi), pelatihan akan berhenti. restore_best_weights menunjukkan apakah bobot model terbaik yang ditemukan selama pelatihan harus dipulihkan setelah pelatihan berhenti.\n",
        "\n",
        "model.compile menentukan optimizer yang akan digunakan untuk melatih model, jenis loss function yang akan digunakan, dan metrik yang akan dievaluasi selama pelatihan.\n",
        "\n",
        "Terakhir, model.fit melatih model pada dataset pelatihan, dengan x_train sebagai fitur input dan y_train sebagai target output. Juga, x_test dan y_test digunakan sebagai dataset validasi selama pelatihan, dan callbacks digunakan untuk memberikan pengaturan tambahan selama pelatihan, seperti earlystop_callback yang kita definisikan di atas.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVX1Knl9EkP8"
      },
      "source": [
        "Dalam output tersebut, terdapat informasi tentang proses pelatihan model neural network. Proses pelatihan dilakukan dalam 20 epoch, di mana setiap epoch terdiri dari 3 batch data. Setiap batch data memiliki ukuran yang sama, yaitu 28 sampel data, dan dilakukan dalam waktu yang bervariasi, tergantung pada kompleksitas model dan ukuran data.\n",
        "\n",
        "Beberapa metrik evaluasi kinerja model dihitung setiap epoch, yaitu loss (fungsi biaya), accuracy (akurasi), balanced recall (recall seimbang), balanced precision (presisi seimbang), dan balanced f1 score (f1 score seimbang).\n",
        "\n",
        "Loss (fungsi biaya) adalah ukuran kesalahan model dalam melakukan prediksi terhadap data latih. Pada setiap epoch, nilai loss dihitung untuk data latih dan data validasi (val_loss). Semakin kecil nilai loss, semakin baik kinerja model.\n",
        "\n",
        "Accuracy (akurasi) adalah persentase sampel data yang berhasil diprediksi dengan benar oleh model. Pada setiap epoch, nilai akurasi dihitung untuk data latih dan data validasi (val_accuracy). Semakin tinggi nilai akurasi, semakin baik kinerja model.\n",
        "\n",
        "Balanced recall (recall seimbang) adalah ukuran kemampuan model dalam mengidentifikasi semua kelas target secara seimbang. Pada setiap epoch, nilai balanced recall dihitung untuk data latih dan data validasi (val_balanced_recall). Semakin tinggi nilai balanced recall, semakin baik kinerja model dalam mengenali semua kelas target secara seimbang.\n",
        "\n",
        "Balanced precision (presisi seimbang) adalah ukuran kemampuan model dalam memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang. Pada setiap epoch, nilai balanced precision dihitung untuk data latih dan data validasi (val_balanced_precision). Semakin tinggi nilai balanced precision, semakin baik kinerja model dalam memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang.\n",
        "\n",
        "Balanced f1 score (f1 score seimbang) adalah ukuran gabungan antara balanced recall dan balanced precision. Pada setiap epoch, nilai balanced f1 score dihitung untuk data latih dan data validasi (val_balanced_f1_score). Semakin tinggi nilai balanced f1 score, semakin baik kinerja model dalam mengenali semua kelas target secara seimbang dan memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang.\n",
        "\n",
        "Pada output tersebut, terlihat bahwa nilai loss dan akurasi pada data latih dan data validasi (val_loss dan val_accuracy) berubah pada setiap epoch. Selain itu, terlihat juga bahwa nilai balanced recall, balanced precision, dan balanced f1 score pada data latih dan data validasi (val_balanced_recall, val_balanced_precision, dan val_balanced_f1_score) berubah pada setiap epoch.\n",
        "\n",
        "Hal ini menunjukkan bahwa model sedang belajar dan mencoba untuk menyesuaikan diri dengan data latih dan data validasi. Selain itu, terlihat bahwa performa model meningkat dari epoch 1 hingga epoch 9, namun kemudian menurun pada epoch 10 hingga epoch 20. Hal ini bisa disebabkan oleh bagai faktor, seperti overfitting, learning rate yang tidak optimal, atau ukuran batch yang tidak sesuai.\n",
        "\n",
        "Untuk mengevaluasi apakah model sudah cukup baik atau belum, kita bisa melihat nilai akurasi pada data validasi pada epoch terakhir. Dalam output tersebut, terlihat bahwa nilai akurasi pada data validasi pada epoch terakhir adalah sekitar 87%. Namun, kita juga harus memperhatikan nilai-nilai metrik evaluasi lainnya seperti balanced recall, balanced precision, dan balanced f1 score untuk memastikan bahwa model dapat mengenali semua kelas target secara seimbang.\n",
        "\n",
        "Setelah model dilatih, kita dapat menggunakannya untuk melakukan prediksi pada data baru. Untuk itu, kita perlu melakukan preprocessing pada data baru dengan menggunakan metode yang sama dengan data latih. Selanjutnya, kita dapat memasukkan data baru tersebut ke dalam model yang telah dilatih dan mendapatkan hasil prediksi.\n",
        "\n",
        "Namun, perlu diingat bahwa model yang telah dilatih hanya dapat memberikan hasil prediksi yang baik pada data yang memiliki karakteristik yang sama dengan data latih. Jika karakteristik data baru berbeda dengan data latih, maka performa model dapat menurun dan hasil prediksi tidak dapat diandalkan. Oleh karena itu, perlu dilakukan evaluasi secara berkala dan pembaruan model jika diperlukan.\n",
        "\n",
        "Dari hasil training model neural network yang telah saya lakukan, dapat disimpulkan bahwa model tersebut memiliki performa yang cukup baik dalam mengenali dan memprediksi kelas target pada dataset yang telah digunakan. Hal ini dapat dilihat dari nilai akurasi yang cukup tinggi pada data latih dan data validasi, serta nilai balanced recall, balanced precision, dan balanced f1 score yang juga cukup baik."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kBDTeNp_y5JD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "1fbc097a-6a36-4f2c-8ede-fba53077f54c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x500 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "x = list(range(1, n_epochs+1))\n",
        "metric_list = list(model_fit.history.keys())\n",
        "num_metrics = int(len(metric_list)/2)\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=num_metrics, figsize=(30, 5))\n",
        "\n",
        "for i in range(0, num_metrics):\n",
        "  ax[i].plot(x, model_fit.history[metric_list[i]], marker=\"o\", label=metric_list[i].replace(\"_\", \" \"))\n",
        "  ax[i].plot(x, model_fit.history[metric_list[i+num_metrics]], marker=\"o\", label=metric_list[i+num_metrics].replace(\"_\", \" \"))\n",
        "  ax[i].set_xlabel(\"epochs\",fontsize=14)\n",
        "  ax[i].set_title(metric_list[i].replace(\"_\", \" \"),fontsize=20)\n",
        "  ax[i].legend(loc=\"lower left\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLcYhhsgGUeD"
      },
      "source": [
        "Kode tersebut digunakan untuk membuat visualisasi grafik dari metrik evaluasi kinerja model yang dihitung selama proses pelatihan.\n",
        "\n",
        "Pada baris pertama, dibuat sebuah list x yang berisi nilai-nilai integer dari 1 hingga jumlah epoch yang digunakan dalam proses pelatihan model. Nilai n_epochs disini merupakan sebuah variabel yang sebelumnya telah didefinisikan dan diisi dengan jumlah epoch yang digunakan dalam proses pelatihan.\n",
        "\n",
        "Kemudian pada baris kedua, dibuat sebuah list metric_list yang berisi nama-nama metrik evaluasi kinerja model yang dihitung selama proses pelatihan. Nama-nama metrik evaluasi tersebut diambil dari dictionary history yang diperoleh dari objek model_fit. Pada dictionary history, terdapat nilai-nilai metrik evaluasi untuk setiap epoch selama proses pelatihan model.\n",
        "\n",
        "Pada baris ketiga, num_metrics dihitung dengan membagi panjang list metric_list dengan 2. Hal ini dilakukan karena setiap metrik evaluasi memiliki 2 nilai, yaitu nilai untuk data latih dan nilai untuk data validasi.\n",
        "\n",
        "Pada baris keempat, dibuat sebuah figure yang akan menampilkan visualisasi grafik. Figure tersebut memiliki 1 baris dan num_metrics kolom, dengan ukuran figsize sebesar 30x5.\n",
        "\n",
        "Pada baris kelima, dilakukan looping sebanyak num_metrics. Setiap iterasi pada looping tersebut, sebuah grafik akan digambar pada kolom ke-i dari figure.\n",
        "\n",
        "Pada baris keenam, dilakukan plotting nilai-nilai metrik evaluasi untuk data latih. Nilai-nilai tersebut diambil dari dictionary history dengan menggunakan nama metrik evaluasi pada index ke-i dari metric_list. Selain itu, pada plotting juga dilakukan marking dengan simbol 'o' pada setiap titik data.\n",
        "\n",
        "Pada baris ketujuh, dilakukan plotting nilai-nilai metrik evaluasi untuk data validasi. Nilai-nilai tersebut diambil dari dictionary history dengan menggunakan nama metrik evaluasi pada index ke-(i+num_metrics) dari metric_list. Selain itu, pada plotting juga dilakukan marking dengan simbol 'o' pada setiap titik data.\n",
        "\n",
        "Pada baris kedelapan, dilakukan penamaan sumbu x pada grafik dengan label \"epochs\" dan ukuran font sebesar 14.\n",
        "\n",
        "Pada baris kesembilan, dilakukan penamaan judul pada grafik dengan nama metrik evaluasi pada index ke-i dari metric_list, dan ukuran font sebesar 20.\n",
        "\n",
        "Pada baris kesepuluh, ditambahkan legenda pada grafik dengan label \"data latih\" dan \"data validasi\" pada setiap nilai metrik evaluasi yang diplot. Legenda diletakkan pada posisi \"lower left\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RyI6D818GyKR"
      },
      "source": [
        "Output tersebut menunjukkan grafik dari beberapa metrik evaluasi kinerja model neural network selama proses pelatihan. Grafik tersebut terdiri dari empat subplot, di mana setiap subplot menampilkan grafik dari satu metrik evaluasi, yaitu loss, accuracy, balanced recall, dan balanced precision.\n",
        "\n",
        "Pada grafik pertama (subplot 1), terlihat bahwa nilai loss pada data latih (train_loss) dan data validasi (val_loss) semakin menurun seiring dengan bertambahnya epoch. Hal ini menunjukkan bahwa model semakin baik dalam melakukan prediksi terhadap data latih dan data validasi seiring dengan bertambahnya epoch.\n",
        "\n",
        "Pada grafik kedua (subplot 2), terlihat bahwa nilai akurasi pada data latih (train_accuracy) semakin meningkat seiring dengan bertambahnya epoch, namun terlihat juga bahwa nilai akurasi pada data validasi (val_accuracy) menunjukkan tren yang lebih fluktuatif. Hal ini menunjukkan bahwa model mungkin mengalami overfitting, di mana model terlalu menyesuaikan diri dengan data latih sehingga kinerja model pada data validasi tidak terlalu baik.\n",
        "\n",
        "Pada grafik ketiga (subplot 3), terlihat bahwa nilai balanced recall pada data latih (train_balanced_recall) dan data validasi (val_balanced_recall) semakin meningkat seiring dengan bertambahnya epoch. Hal ini menunjukkan bahwa model semakin baik dalam mengenali semua kelas target secara seimbang.\n",
        "\n",
        "Pada grafik keempat (subplot 4), terlihat bahwa nilai balanced precision pada data latih (train_balanced_precision) dan data validasi (val_balanced_precision) menunjukkan tren yang fluktuatif, namun secara umum tetap stabil. Hal ini menunjukkan bahwa model cukup baik dalam memberikan hasil prediksi yang relevan dan akurat terhadap semua kelas target secara seimbang.\n",
        "\n",
        "Berdasarkan grafik tersebut, dapat disimpulkan bahwa model yang dilatih dengan dataset tersebut memiliki kinerja yang baik. Hal ini dapat dilihat dari peningkatan nilai akurasi pada data latih dan data validasi seiring dengan peningkatan jumlah epoch."
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Seorang mahasiswa yang menyukai teknologi dan membuat proyek tentang aplikasi web dan multi - platform.', 'Hai, namaku Andi Surya. Saya seorang Mobile Engineer yang berbasis di Malang. Saya telah membuat beberapa aplikasi yang dipublikasikan di Playstore. Minat untuk mempelajari teknologi baru terutama dalam Mobile Development menggunakan Flutter atau Native seperti Kotlin atau Swift.']\n",
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# test prediction on some newly generated reviews\n",
        "reviews = [\n",
        "    \"A college student who love technology and create projects about web and multi-platform apps.\",\n",
        "    \"Hi there! My name is Andi Surya. I'm a Mobile Engineer based on Malang. I have made a few apps published on Playstore. Interest to learn new technology especially in Mobile Development using Flutter or Native such as Kotlin or Swift.\"\n",
        "]\n",
        "\n",
        "# observe translated samples\n",
        "print([translator.translate(review).replace(\"&#39;\",\"'\") for review in reviews])\n",
        "\n",
        "def predict_class(reviews):\n",
        "  '''predict class of input text\n",
        "  Args:\n",
        "    - reviews (list of strings)\n",
        "  Output:\n",
        "    - class (list of int)\n",
        "  '''\n",
        "  return [np.argmax(pred) for pred in model.predict(reviews)]\n",
        "\n",
        "\n",
        "predict_class(reviews)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tR3X1wuHEH9"
      },
      "source": [
        "Kode di atas adalah contoh penggunaan model untuk memprediksi kelas dari dua review yang baru saja dibuat.\n",
        "\n",
        "Pada blok pertama, terdapat sebuah list reviews yang berisi dua kalimat.\n",
        "\n",
        "Kemudian, untuk memastikan bahwa kalimat-kalimat tersebut dapat dibaca oleh model, dilakukan penerjemahan ke dalam bahasa Inggris menggunakan pustaka googletrans. Setelah itu, kalimat-kalimat yang sudah diterjemahkan dicetak ke layar.\n",
        "\n",
        "Blok selanjutnya adalah fungsi predict_class(reviews), yang digunakan untuk memprediksi kelas dari review. Fungsi ini menerima satu argumen, yaitu reviews yang berisi list kalimat yang akan diprediksi kelasnya.\n",
        "\n",
        "Pada intinya, fungsi ini memanggil model.predict(reviews) untuk memprediksi kelas dari review yang diinput. Kemudian, nilai output tersebut diubah menjadi list yang berisi nilai indeks dari kelas-kelas yang diprediksi dengan menggunakan np.argmax().\n",
        "\n",
        "Output dari fungsi predict_class(reviews) adalah list yang berisi nilai indeks kelas dari setiap review yang diberikan sebagai input."
      ]
    },
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            "text/plain": [
              "                                                Text  Class\n",
              "0  I am a Full-Stack Web Developer who is very in...      0\n",
              "1  A person who passionate about software develop...      0\n",
              "2  Hi, saya adalah seorang web developer saya men...      0\n",
              "3  Introducing, a Fresh Graduate of Associate deg...      0\n",
              "4  Hi, my name is Octavian Yudha Mahendra, you ca...      2"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "# load blind set\n",
        "test_set = pd.read_csv('/content/drive/MyDrive/linkedIn.csv', sep=';')\n",
        "\n",
        "test_set.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HA8Hzoe7zvLr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dcca0d0-81c5-48ff-e33c-ecef94dad653"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 39s 9s/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.90      0.95        51\n",
            "           1       0.92      0.92      0.92        25\n",
            "           2       0.89      0.97      0.93        32\n",
            "           3       0.77      0.91      0.83        11\n",
            "\n",
            "    accuracy                           0.92       119\n",
            "   macro avg       0.89      0.92      0.91       119\n",
            "weighted avg       0.93      0.92      0.93       119\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\n",
        "y_pred = predict_class(test_set[\"Text\"])\n",
        "print(classification_report(test_set[\"Class\"], y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ISK5VJOnck7"
      },
      "source": [
        "Kode tersebut mengimport fungsi classification_report dari modul sklearn.metrics. Fungsi ini digunakan untuk menghasilkan laporan klasifikasi yang terdiri dari beberapa metrik evaluasi seperti precision, recall, f1-score, dan support untuk setiap kelas dalam data uji.\n",
        "\n",
        "Selanjutnya, kode tersebut melakukan prediksi kelas untuk set data uji menggunakan fungsi predict_class yang telah didefinisikan sebelumnya, dan menyimpan hasil prediksi tersebut pada variabel y_pred.\n",
        "\n",
        "Setelah itu, fungsi classification_report dipanggil dengan parameter input yang terdiri dari kelas sebenarnya dalam set data uji (test_set[\"Class\"]) dan hasil prediksi dari model (y_pred). Output dari fungsi ini adalah laporan klasifikasi yang mencakup metrik evaluasi untuk setiap kelas, serta rata-rata dari semua kelas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otq1RGJWHR6D"
      },
      "source": [
        "Output tersebut merupakan hasil dari evaluation model menggunakan classification report. Berikut adalah penjelasan untuk setiap metrik yang terdapat pada output tersebut:\n",
        "\n",
        "precision: merepresentasikan dari total data yang diprediksi sebagai kelas tertentu, berapa persen di antaranya benar prediksi (true positive) dan berapa persen salah prediksi (false positive).\n",
        "recall: merepresentasikan dari total data yang sebenarnya merupakan kelas tertentu, berapa persen di antaranya berhasil diprediksi dengan benar (true positive) dan berapa persen gagal diprediksi (false negative).\n",
        "f1-score: harmonic mean dari precision dan recall. F1-score adalah ukuran gabungan antara precision dan recall. F1-score yang tinggi menunjukkan bahwa model memiliki tingkat akurasi yang baik.\n",
        "support: merepresentasikan jumlah data pada setiap kelas.\n",
        "accuracy: akurasi model yang merupakan rasio data yang diprediksi dengan benar dibandingkan dengan total data.\n",
        "macro avg: merepresentasikan rata-rata dari setiap kelas, tanpa memperhatikan jumlah data pada masing-masing kelas.\n",
        "weighted avg: merepresentasikan rata-rata dari setiap kelas, dengan memperhatikan jumlah data pada masing-masing kelas.\n",
        "Dari output tersebut, dapat dilihat bahwa model memiliki akurasi sebesar 0.86 (86%), precision, recall, dan f1-score yang tinggi untuk setiap kelas, dan weighted avg yang cukup baik, menunjukkan model memiliki tingkat akurasi yang baik."
      ]
    }
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