Classification_text_messange_with_LSTM.ipynb

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      "source": [
        "# SUBMISSION PROJECT DICODING \"Belajar Pengembangan Machine Learning\"\n",
        "## DATA DIRI\n",
        "\n",
        "*  Nama= Awang Mulya Nugrawan\n",
        "*  username DICODING= awang_nugrawan\n",
        "*  email = awangmulyanugrawan@gmail.com\n",
        "*  No.hp = 082191862002\n",
        "*  Asal= Makassar, Sulawesi Selatan\n",
        "*  Status = Mahasiswa Sisfor Universitas Hasanuddin\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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        "id": "ZWJdspdVb8bg"
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      "source": [
        "# LIBRARY"
      ],
      "metadata": {
        "id": "i5fbrHM5c-0O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Embedding,LSTM,Dropout\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "feI9_6nxdGzS"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
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      "source": [
        "#Dataset "
      ],
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      }
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "zd3lOULdsaoQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/drive/MyDrive/Machine learning/SPAM text message 20170820 - Data.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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              "     Category                                            Message\n",
              "0         ham  Go until jurong point, crazy.. Available only ...\n",
              "1         ham                      Ok lar... Joking wif u oni...\n",
              "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3         ham  U dun say so early hor... U c already then say...\n",
              "4         ham  Nah I don't think he goes to usf, he lives aro...\n",
              "...       ...                                                ...\n",
              "5567     spam  This is the 2nd time we have tried 2 contact u...\n",
              "5568      ham               Will ü b going to esplanade fr home?\n",
              "5569      ham  Pity, * was in mood for that. So...any other s...\n",
              "5570      ham  The guy did some bitching but I acted like i'd...\n",
              "5571      ham                         Rofl. Its true to its name\n",
              "\n",
              "[5572 rows x 2 columns]"
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              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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              "      <td>ham</td>\n",
              "      <td>Will ü b going to esplanade fr home?</td>\n",
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              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>ham</td>\n",
              "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
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              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>ham</td>\n",
              "      <td>The guy did some bitching but I acted like i'd...</td>\n",
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              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>ham</td>\n",
              "      <td>Rofl. Its true to its name</td>\n",
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      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Category  5572 non-null   object\n",
            " 1   Message   5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "#ENCODING"
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      "metadata": {
        "id": "-xTPs6CDePBE"
      }
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    {
      "cell_type": "code",
      "source": [
        "df.Category.replace('ham' , 1 ,inplace = True)\n",
        "df.Category.replace('spam' , 0 ,inplace = True)"
      ],
      "metadata": {
        "id": "DBfSJyasFOiM"
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      "execution_count": 27,
      "outputs": []
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              "      Category                                            Message\n",
              "0            1  Go until jurong point, crazy.. Available only ...\n",
              "1            1                      Ok lar... Joking wif u oni...\n",
              "2            0  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3            1  U dun say so early hor... U c already then say...\n",
              "4            1  Nah I don't think he goes to usf, he lives aro...\n",
              "...        ...                                                ...\n",
              "5567         0  This is the 2nd time we have tried 2 contact u...\n",
              "5568         1               Will ü b going to esplanade fr home?\n",
              "5569         1  Pity, * was in mood for that. So...any other s...\n",
              "5570         1  The guy did some bitching but I acted like i'd...\n",
              "5571         1                         Rofl. Its true to its name\n",
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      "source": [
        "import re\n",
        "#hapus tanda baca\n",
        "df[\"Message\"] = df[\"Message\"].apply(lambda x: re.sub(r'[^\\w\\s]', '', x))\n"
      ],
      "metadata": {
        "id": "wOWLO9mQiy26"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "S0VYl1KWjT9h",
        "outputId": "99f640fc-01df-4ce9-b337-40ba56fc418f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Category                                            Message\n",
              "0            1  Go until jurong point crazy Available only in ...\n",
              "1            1                            Ok lar Joking wif u oni\n",
              "2            0  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3            1        U dun say so early hor U c already then say\n",
              "4            1  Nah I dont think he goes to usf he lives aroun...\n",
              "...        ...                                                ...\n",
              "5567         0  This is the 2nd time we have tried 2 contact u...\n",
              "5568         1                Will ü b going to esplanade fr home\n",
              "5569         1  Pity  was in mood for that Soany other suggest...\n",
              "5570         1  The guy did some bitching but I acted like id ...\n",
              "5571         1                          Rofl Its true to its name\n",
              "\n",
              "[5572 rows x 2 columns]"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>Go until jurong point crazy Available only in ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>U dun say so early hor U c already then say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Nah I dont think he goes to usf he lives aroun...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5567</th>\n",
              "      <td>0</td>\n",
              "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5568</th>\n",
              "      <td>1</td>\n",
              "      <td>Will ü b going to esplanade fr home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>1</td>\n",
              "      <td>Pity  was in mood for that Soany other suggest...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>1</td>\n",
              "      <td>The guy did some bitching but I acted like id ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>1</td>\n",
              "      <td>Rofl Its true to its name</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5572 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aab1aad7-bd41-4ddf-8f87-dfb041f8ae43')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\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-aab1aad7-bd41-4ddf-8f87-dfb041f8ae43 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-aab1aad7-bd41-4ddf-8f87-dfb041f8ae43');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#detelete stopwards\n",
        "from gensim.parsing.preprocessing import remove_stopwords\n",
        "df[\"Message\"] = df[\"Message\"].apply(lambda x: remove_stopwords(x))"
      ],
      "metadata": {
        "id": "kG_CcDEfqsLZ"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "3lUO3nrtq4S8",
        "outputId": "76b3ca51-64dd-4bb1-c150-220de94703d5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Category                                            Message\n",
              "0            1  Go jurong point crazy Available bugis n great ...\n",
              "1            1                            Ok lar Joking wif u oni\n",
              "2            0  Free entry 2 wkly comp win FA Cup final tkts 2...\n",
              "3            1                                U dun early hor U c\n",
              "4            1                    Nah I dont think goes usf lives\n",
              "...        ...                                                ...\n",
              "5567         0  This 2nd time tried 2 contact u U won 750 Poun...\n",
              "5568         1                   Will ü b going esplanade fr home\n",
              "5569         1                        Pity mood Soany suggestions\n",
              "5570         1  The guy bitching I acted like id interested bu...\n",
              "5571         1                                      Rofl Its true\n",
              "\n",
              "[5572 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2333d69f-decd-4ffb-82b4-86a289077702\">\n",
              "    <div 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>Category</th>\n",
              "      <th>Message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Go jurong point crazy Available bugis n great ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Ok lar Joking wif u oni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>Free entry 2 wkly comp win FA Cup final tkts 2...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>U dun early hor U c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Nah I dont think goes usf lives</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5567</th>\n",
              "      <td>0</td>\n",
              "      <td>This 2nd time tried 2 contact u U won 750 Poun...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5568</th>\n",
              "      <td>1</td>\n",
              "      <td>Will ü b going esplanade fr home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>1</td>\n",
              "      <td>Pity mood Soany suggestions</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>1</td>\n",
              "      <td>The guy bitching I acted like id interested bu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>1</td>\n",
              "      <td>Rofl Its true</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5572 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2333d69f-decd-4ffb-82b4-86a289077702')\"\n",
              "              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",
              "      flex-wrap:wrap;\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",
              "    [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-2333d69f-decd-4ffb-82b4-86a289077702 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-2333d69f-decd-4ffb-82b4-86a289077702');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Split dataset and Tokenization"
      ],
      "metadata": {
        "id": "saEUPIJBeTaQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test,y_train,y_test = train_test_split(df['Message'],df['Category'],test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "2f16MasxGOqJ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=1200, oov_token='-')\n",
        "tokenizer.fit_on_texts(X_train) #hanya digunakan untuk data training saja untuk menghindari kemungkinan terjadinya overfitting\n",
        " \n",
        "sekuens_latih = tokenizer.texts_to_sequences(X_train)\n",
        "sekuens_test = tokenizer.texts_to_sequences(X_test)\n",
        " \n",
        "padded_latih = pad_sequences(sekuens_latih,maxlen=100,padding ='post', truncating='post') \n",
        "padded_test = pad_sequences(sekuens_test,maxlen=100,padding ='post', truncating='post')"
      ],
      "metadata": {
        "id": "O6yqrqY3GQa5"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Membangun arsitektur model"
      ],
      "metadata": {
        "id": "rwMnsGHVfIqq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    Embedding(1200, 20, input_length=100),\n",
        "    LSTM(100, return_sequences=True),\n",
        "    Dropout(0.5),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n"
      ],
      "metadata": {
        "id": "LlyYCchWJ8no"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Compile dan latih model dengan model.compile dan model.fit hingga mendapatkan akurasi yang diinginkan."
      ],
      "metadata": {
        "id": "6skVi6DLfSBC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "y0iyv3jpfT3k"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#IMPLEMENTASI CALLBACK\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.95):\n",
        "      print(\"\\nAkurasi telah mencapai >95%!\")\n",
        "      self.model.stop_training = True\n",
        "callbacks = myCallback()"
      ],
      "metadata": {
        "id": "myTSXnU9L1YO"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(\n",
        "    padded_latih, y_train, \n",
        "    validation_data=(padded_test, y_test),\n",
        "    steps_per_epoch=32,  # berapa batch yang akan dieksekusi pada setiap epoch\n",
        "    epochs=15,\n",
        "    validation_steps=8,  # berapa batch yang akan dieksekusi pada setiap epoch   \n",
        "    batch_size=128,\n",
        "    callbacks=[callbacks], #implementasi callback\n",
        "    validation_split=0.2, \n",
        "    verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pS4gHKehVEDI",
        "outputId": "ef2d5a93-81a1-46ca-dd49-23a290851ee6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - 14s 346ms/step - loss: 0.5027 - accuracy: 0.8547 - val_loss: 0.3814 - val_accuracy: 0.8730\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 10s 327ms/step - loss: 0.3926 - accuracy: 0.8689 - val_loss: 0.3631 - val_accuracy: 0.8730\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 10s 327ms/step - loss: 0.2550 - accuracy: 0.9090 - val_loss: 0.1455 - val_accuracy: 0.9598\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.1327 - accuracy: 0.9624\n",
            "Akurasi telah mencapai >95%!\n",
            "32/32 [==============================] - 10s 322ms/step - loss: 0.1327 - accuracy: 0.9624 - val_loss: 0.1294 - val_accuracy: 0.9618\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plot loss dan akurasi pada saat training dan validation."
      ],
      "metadata": {
        "id": "YHwxRXpUgazP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(hist.history['accuracy'])\n",
        "plt.plot(hist.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1Ba8C02ta9da",
        "outputId": "96908aa3-4851-452b-dfb6-0980cf2cd40b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "tQFq9jGKbCtE",
        "outputId": "4a7e0b55-2228-4641-b992-0faa6755f834"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}