Project_DinusBot_Training_Model.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **Project Akhir - DinusBot**\n",
        "\n",
        "Project ini mengangkat permasalahan dari sulitnya mencari informasi mengenai seputar kampus oleh mahasiswa dari website halaman resmi salah satu perguruan tinggi yaitu Universitas Dian Nuswantoro. Yang mana project DinusBot ini dibuat sebagai solusi untuk mahasiswa UDINUS sebagai kemudahan dalam mencari informasi yang dibutuhkan seputar kampus. Project AI pada domain NLP yang digarap menggunakan algoritma RNN-LSTM yang mana merupakan pengembangan lanjutan dari algoritma RNN pada pembuatan chatbot AI ini. Project ini digarap oleh kelompok 4 dalam waktu kurang lebih selama 1 bulan lamanya yang beranggotakan:\n",
        "```\n",
        "Anggota Kelompok 4: \n",
        "\n",
        "1.   Nabila Jania Fitri\n",
        "2.   Nia Nur Lailiyah\n",
        "3.   Diaz Islami\n",
        "4.   Giga Fajar Septianto\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "## **Menyiapkan library yang dibutuhkan**\n",
        "\n"
      ],
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          "text": [
            "\u001b[K     |████████████████████████████████| 32.8 MB 1.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 96 kB 6.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.7 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.9.0 requires jedi>=0.10, which is not installed.\n",
            "flask 1.1.4 requires click<8.0,>=5.1, but you have click 8.1.3 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install SpeechRecognition\n",
        "!pip -q install gtts"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Libraries\n",
        "import json\n",
        "import nltk\n",
        "import time\n",
        "import random\n",
        "import string\n",
        "import pickle\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gtts import gTTS\n",
        "from io import BytesIO\n",
        "import tensorflow as tf\n",
        "import IPython.display as ipd\n",
        "import speech_recognition as sr \n",
        "import matplotlib.pyplot as plt\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from tensorflow.keras.models import Model\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.layers import Input, Embedding, LSTM\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Flatten, Dense, GlobalMaxPool1D"
      ],
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        "id": "Q0rtg3jFqOKK"
      },
      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "# Package sentence tokenizer\n",
        "nltk.download('omw-1.4')\n",
        "# Package lemmatization\n",
        "nltk.download('punkt')\n",
        "# Package multilingual wordnet data\n",
        "nltk.download('wordnet')"
      ],
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          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
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              "True"
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      "cell_type": "markdown",
      "source": [
        "## **Load dataset intent.json**"
      ],
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      "source": [
        "# Importing the dataset\n",
        "with open('/content/intents.json') as content:\n",
        "  data1 = json.load(content)\n",
        "\n",
        "# Mendapatkan semua data ke dalam list\n",
        "tags = []\n",
        "inputs = []\n",
        "responses = {}\n",
        "words = []\n",
        "classes = []\n",
        "documents = []\n",
        "ignore_words = ['?', '!']\n",
        "\n",
        "for intent in data1['intents']:\n",
        "  responses[intent['tag']]=intent['responses']\n",
        "  for lines in intent['patterns']:\n",
        "    inputs.append(lines)\n",
        "    tags.append(intent['tag'])\n",
        "    for pattern in intent['patterns']:\n",
        "      w = nltk.word_tokenize(pattern)\n",
        "      words.extend(w)\n",
        "      documents.append((w, intent['tag']))\n",
        "      # add to our classes list\n",
        "      if intent['tag'] not in classes:\n",
        "        classes.append(intent['tag'])\n",
        "\n",
        "# Konversi data json ke dalam dataframe\n",
        "data = pd.DataFrame({\"patterns\":inputs, \"tags\":tags})"
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      "source": [
        "# Mencetak keseluruhan data\n",
        "data"
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              "                                             patterns  \\\n",
              "0                                               hallo   \n",
              "1                                                 hai   \n",
              "2                                                halo   \n",
              "3                                                 hei   \n",
              "4                                                  hi   \n",
              "..                                                ...   \n",
              "90  Bagaimana mekanisme aktivasi siadin atau reset...   \n",
              "91     Bagaimana mekanisme aktivasi siadin mahasiswa?   \n",
              "92  Bagaimana mekanisme reset password siadin maha...   \n",
              "93  Kemana saya harus mengurus masalah aktivasi si...   \n",
              "94          Bagaimana cara mahasiswa login ke siadin?   \n",
              "\n",
              "                                 tags  \n",
              "0                            greeting  \n",
              "1                            greeting  \n",
              "2                            greeting  \n",
              "3                            greeting  \n",
              "4                            greeting  \n",
              "..                                ...  \n",
              "90                   jadwal_input_krs  \n",
              "91                   jadwal_input_krs  \n",
              "92                   jadwal_input_krs  \n",
              "93  masalah_aktivasi_siadin_mahasiswa  \n",
              "94        cara_login_siadin_mahasiswa  \n",
              "\n",
              "[95 rows x 2 columns]"
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              "      <td>Kemana saya harus mengurus masalah aktivasi si...</td>\n",
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              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Bagaimana cara mahasiswa login ke siadin?</td>\n",
              "      <td>cara_login_siadin_mahasiswa</td>\n",
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        "# Mencetak 5 baris data pertama dari yang paling atas\n",
        "data.head()"
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              "  patterns      tags\n",
              "0    hallo  greeting\n",
              "1      hai  greeting\n",
              "2     halo  greeting\n",
              "3      hei  greeting\n",
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              "          const element = document.querySelector('#df-e75d74f2-d463-49e4-9dcb-a37e0613a424');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mencetak data dari baris ke-90 sampai akhir\n",
        "data[90:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9tCpc_0SrN-4",
        "outputId": "535b3456-ea30-4576-d60c-05f92688c3df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             patterns  \\\n",
              "90  Bagaimana mekanisme aktivasi siadin atau reset...   \n",
              "91     Bagaimana mekanisme aktivasi siadin mahasiswa?   \n",
              "92  Bagaimana mekanisme reset password siadin maha...   \n",
              "93  Kemana saya harus mengurus masalah aktivasi si...   \n",
              "94          Bagaimana cara mahasiswa login ke siadin?   \n",
              "\n",
              "                                 tags  \n",
              "90                   jadwal_input_krs  \n",
              "91                   jadwal_input_krs  \n",
              "92                   jadwal_input_krs  \n",
              "93  masalah_aktivasi_siadin_mahasiswa  \n",
              "94        cara_login_siadin_mahasiswa  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c8331fbc-6ccb-4a9f-afa2-043c4a7113fd\">\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>patterns</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Bagaimana mekanisme aktivasi siadin atau reset...</td>\n",
              "      <td>jadwal_input_krs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Bagaimana mekanisme aktivasi siadin mahasiswa?</td>\n",
              "      <td>jadwal_input_krs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Bagaimana mekanisme reset password siadin maha...</td>\n",
              "      <td>jadwal_input_krs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Kemana saya harus mengurus masalah aktivasi si...</td>\n",
              "      <td>masalah_aktivasi_siadin_mahasiswa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Bagaimana cara mahasiswa login ke siadin?</td>\n",
              "      <td>cara_login_siadin_mahasiswa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8331fbc-6ccb-4a9f-afa2-043c4a7113fd')\"\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-c8331fbc-6ccb-4a9f-afa2-043c4a7113fd 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-c8331fbc-6ccb-4a9f-afa2-043c4a7113fd');\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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Preproses data**"
      ],
      "metadata": {
        "id": "FmjoRzHVrnn4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghilangkan Punktuasi\n",
        "data['patterns'] = data['patterns'].apply(lambda wrd:[ltrs.lower() for ltrs in wrd if ltrs not in string.punctuation])\n",
        "data['patterns'] = data['patterns'].apply(lambda wrd: ''.join(wrd))"
      ],
      "metadata": {
        "id": "HhktLeN3raP1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Lematisasi\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
        "words = sorted(list(set(words)))\n",
        "\n",
        "print(len(words), \"unique lemmatized words\", words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfaJo98csRaj",
        "outputId": "2c10e834-cb97-4a46-b61a-83b1737951eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "184 unique lemmatized words ['&', '(', ')', ',', '/', '2', '4', 'ada', 'adakah', 'afternoon', 'aktivasi', 'alamat', 'alumnus', 'apa', 'apakah', 'atau', 'bagaiamana', 'bagaimana', 'bai', 'banyak', 'barang', 'beasiswa', 'beban', 'berapa', 'berobat', 'bisa', 'bkd', 'bro', 'bukan', 'buku', 'bye', 'byee', 'cara', 'career', 'center', 'dadah', 'dah', 'dan', 'dapat', 'dari', 'dengan', 'di', 'diadakan', 'dian', 'diikut', 'dijadikan', 'dikelola', 'dimaksud', 'dimana', 'dimiliki', 'diperlukan', 'dosen', 'fasilitas', 'fasilitias', 'fasilsitas', 'good', 'hai', 'hallo', 'halo', 'harus', 'hei', 'helm', 'hi', 'hilang', 'hy', 'ilmiah', 'info', 'informasi', 'input', 'itu', 'jadwal', 'jenis', 'jenis-jenis', 'jika', 'jobfair', 'judul', 'jumpa', 'kapan', 'kartu', 'kasih', 'kawan', 'ke', 'kegiatan', 'kelas', 'kemana', 'kendaraan', 'kerja', 'kinerja', 'kita', 'koleksi', 'komunitas', 'konseling', 'kontak', 'kr', 'ktm', 'layanan', 'letak', 'login', 'lokasi', 'macam', 'mahasiswa', 'makasih', 'malam', 'masalah', 'mekanisme', 'melamar', 'melihat', 'membayar', 'member', 'membuat', 'mendapatkan', 'mengakses', 'mengetahui', 'mengurus', 'mengusulkan', 'menjadi', 'misi', 'mobil', 'morning', 'motor', 'nama', 'nuswantoro', 'nuwantoro', 'obatnya', 'oleh', 'organisasi', 'pagi', 'parkir', 'password', 'pelayanan', 'peneliitian/karya', 'penelitian', 'pengumuman', 'pergi', 'perlukah', 'perpustakaan', 'person', 'poli', 'poliklinik', 'reset', 'roda', 'saat', 'saja', 'sampai', 'saya', 'seandainya', 'sebagai', 'see', 'sejarah', 'selamat', 'seperti', 'si', 'siadin', 'siang', 'sih', 'sobat', 'sore', 'standar', 'statistik', 'sudah', 'syarat', 'syarat-syarat', 'tanda', 'tentang', 'terima', 'terjadi', 'terlaksana', 'tersedia', 'tertuang', 'tetapi', 'thank', 'thanks', 'tinggal', 'udinus', 'unit', 'universitas', 'university', 'untuk', 'visi', 'wajib', 'wali', 'website', 'yang', 'you']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menyortir data kelas tag\n",
        "classes = sorted(list(set(classes)))\n",
        "print (len(classes), \"classes\", classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_FL6lxGsd6C",
        "outputId": "57e36890-0176-4802-b14a-956878ba9eeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "43 classes ['beban_kerja_dosen', 'berobat_layanan_poliklinik', 'cara_login_siadin_mahasiswa', 'career_center_udinus', 'daftar_member_career_center_udinus', 'dosen_wali', 'fasilitas_udinus', 'goodbye', 'greeting', 'info_statistik_penelitian', 'informasi_bkd', 'informasi_dosen_udinus', 'informasi_jobfair_udinus', 'jadwal_career_center_udinus', 'jadwal_input_krs', 'jenis_beasiswa_udinus', 'jenis_poli_poliklinik', 'jobfair_udinus', 'jumlah_organisasi_udinus', 'jumlah_ukm_udinus', 'kehilangan_tempat_parkir', 'komunitas_udinus', 'konseling_alumni_career_center_udinus', 'kontak_career_center_udinus', 'lamar_kerja_career_center_udinus', 'lokasi_career_center_udinus', 'lokasi_parkir', 'lokasi_udinus', 'macam_kegiatan_penelitian', 'masalah_aktivasi_siadin_mahasiswa', 'member_career_center_udinus', 'parkir_roda_dua_udinus', 'parkir_roda_empat_udinus', 'pencarian_informasi_dosen_udinus', 'pengumuman_dosen_kelas', 'sejarah_udinus', 'syarat_buat_ktm', 'syarat_layanan_poliklinik', 'syarat_parkir_udinus', 'terimakasih', 'udinus', 'usul_judul_buku', 'visi_misi_udinus']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mencetak jumlah keseluruhan data teks\n",
        "# documents = kombinasi antara patterns and intents\n",
        "print (len(documents), \"documents\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6mFFRjyswhM",
        "outputId": "ac225076-181c-43e7-dfd9-a4686bfc6ee7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "523 documents\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenisasi Data\n",
        "tokenizer = Tokenizer(num_words=2000)\n",
        "tokenizer.fit_on_texts(data['patterns'])\n",
        "train = tokenizer.texts_to_sequences(data['patterns'])\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wDl9MyvtEbP",
        "outputId": "1713332f-c9e2-44f4-aadc-476a4a9c7cd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[71],\n",
              " [72],\n",
              " [73],\n",
              " [74],\n",
              " [75],\n",
              " [76],\n",
              " [77],\n",
              " [78],\n",
              " [79],\n",
              " [80],\n",
              " [81],\n",
              " [82],\n",
              " [83],\n",
              " [84],\n",
              " [85],\n",
              " [86],\n",
              " [87],\n",
              " [88],\n",
              " [42],\n",
              " [89],\n",
              " [90, 42],\n",
              " [91, 92],\n",
              " [93, 94],\n",
              " [95],\n",
              " [96, 43],\n",
              " [97],\n",
              " [98, 43],\n",
              " [44, 45],\n",
              " [99],\n",
              " [44, 45, 100],\n",
              " [1],\n",
              " [1],\n",
              " [101, 46, 102],\n",
              " [46, 103, 104],\n",
              " [2, 25, 1],\n",
              " [26, 1],\n",
              " [9, 26, 1],\n",
              " [105, 2, 26, 1],\n",
              " [2, 27, 28, 1],\n",
              " [27, 28, 1],\n",
              " [27, 20, 28],\n",
              " [7, 1],\n",
              " [7, 47, 1],\n",
              " [7, 48, 1],\n",
              " [47, 1],\n",
              " [48, 1],\n",
              " [106, 1],\n",
              " [2, 6, 49, 3, 1],\n",
              " [107, 4, 50, 1],\n",
              " [2, 6, 49, 4, 50, 1],\n",
              " [29, 2, 6, 4, 30, 13, 31, 108, 109, 5, 32, 1],\n",
              " [29, 2, 6, 4, 30, 13, 31, 32, 1],\n",
              " [2, 6, 29, 4, 30, 13, 31, 32],\n",
              " [33, 14, 110, 111, 51, 34, 1, 112, 14, 113, 13, 35, 114],\n",
              " [7, 16, 115, 20, 116, 3, 1],\n",
              " [10, 21, 117, 118, 4, 10, 3, 34, 1],\n",
              " [119, 5, 52, 2, 4, 120, 121, 122, 35, 3, 34, 1],\n",
              " [10, 21, 123, 53, 124, 125, 4, 36, 126, 37, 5, 1],\n",
              " [2, 6, 127, 128, 4, 129, 1],\n",
              " [10, 21, 130, 53, 5, 3, 1],\n",
              " [10, 21, 131, 5, 3, 1],\n",
              " [132, 2, 6, 4, 10, 3, 1],\n",
              " [7, 54, 36, 55, 38, 56, 15, 3, 1],\n",
              " [2, 133, 11, 12, 1, 25],\n",
              " [2, 25, 11, 12, 1],\n",
              " [22, 17, 57, 58, 11, 12, 1, 134, 135, 59, 1],\n",
              " [9, 57, 58, 11, 12, 1],\n",
              " [7, 136, 11, 12, 1],\n",
              " [137, 39, 138, 139, 3, 140, 11, 12, 1],\n",
              " [141, 142, 143, 11, 12, 1],\n",
              " [144, 6, 35, 11, 12, 1, 145],\n",
              " [22, 10, 146, 147, 13, 59, 1],\n",
              " [7, 6, 16, 60, 61, 148, 3, 1],\n",
              " [7, 6, 16, 60, 61, 149, 3, 1],\n",
              " [2, 6, 52, 16, 3, 1],\n",
              " [9, 33, 62, 150, 151, 4, 152, 153, 16, 3, 1],\n",
              " [22, 17, 154, 155, 156, 157, 3, 158, 1],\n",
              " [7, 17, 159, 38, 63, 1],\n",
              " [9, 13, 160, 161, 162, 163, 4, 164, 165, 37, 1],\n",
              " [22, 10, 63, 4, 166, 37, 1],\n",
              " [7, 14, 17, 40, 167, 15, 168, 14],\n",
              " [2, 4, 64, 41, 65, 66, 15],\n",
              " [2, 4, 64, 41, 67],\n",
              " [2, 6, 4, 68, 3, 65, 66, 15],\n",
              " [2, 6, 4, 68, 3, 67],\n",
              " [7, 54, 36, 55, 38, 56, 15, 3, 1],\n",
              " [9, 39, 5, 40, 169, 170, 15, 171, 3, 8],\n",
              " [7, 5, 17, 40, 172, 173, 174],\n",
              " [9, 18, 19, 8, 20, 69, 23, 24, 8, 5],\n",
              " [9, 18, 19, 8, 20, 23, 24, 8, 5],\n",
              " [9, 18, 19, 8, 69, 23, 24, 8, 5],\n",
              " [9, 18, 19, 8, 5],\n",
              " [9, 18, 23, 24, 8, 5],\n",
              " [175, 14, 176, 177, 70, 19, 8, 5, 33, 178, 62, 70, 41, 8, 14],\n",
              " [9, 39, 5, 179, 51, 8]]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menerapkan padding \n",
        "x_train = pad_sequences(train)\n",
        "print(x_train) # Padding Sequences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKhitQk2tP4i",
        "outputId": "78e8992f-a668-4915-92b7-4fcb2d3e9894"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0   0   0 ...   0   0  71]\n",
            " [  0   0   0 ...   0   0  72]\n",
            " [  0   0   0 ...   0   0  73]\n",
            " ...\n",
            " [  0   0   0 ...  24   8   5]\n",
            " [175  14 176 ...  41   8  14]\n",
            " [  0   0   0 ... 179  51   8]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding output \n",
        "le =LabelEncoder()\n",
        "y_train = le.fit_transform(data['tags'])\n",
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JKSiF5ztymV",
        "outputId": "48a013c2-24a6-4d18-d710-fff5d2b08387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7\n",
            "  7 39 39 39 39 39 40 40 40 40 40 35 35 35 42 42 42 27 27 27 27 27  6  6\n",
            "  6  6 36 36 36  1 26 16 37 28 15 19 18 21 11  3  3 30  4 25 24 23 13 22\n",
            " 31 32 38 20 41 12  9 17  5  0  0 10 10 33 34 14 14 14 14 14 14 29  2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Input Length, Output Length and Vocabulary**"
      ],
      "metadata": {
        "id": "GVyg6INuvq8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input length\n",
        "input_shape = x_train.shape[1]\n",
        "print(input_shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVgv1RFgvSY6",
        "outputId": "bc2ff099-e606-46b9-9c1c-593be677f260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define vocabulary\n",
        "vocabulary = len(tokenizer.word_index)\n",
        "print(\"number of unique words : \", vocabulary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXqRP7gAvTh-",
        "outputId": "06c534e3-4812-4754-8a03-fcaa7e42bb88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of unique words :  179\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# output length\n",
        "output_length = le.classes_.shape[0]\n",
        "print(\"output length: \", output_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlCjzXn6vWNf",
        "outputId": "344f2408-eb89-4d37-9919-28f019e5ee06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "output length:  43\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Save Model Words & Classes**"
      ],
      "metadata": {
        "id": "-ZOWfN77vtLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(words, open('/content/Model/words.pkl','wb'))\n",
        "pickle.dump(classes, open('/content/Model/classes.pkl','wb'))"
      ],
      "metadata": {
        "id": "5QYZF6qYvZRq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Save Label Encoder & Tokenizer**"
      ],
      "metadata": {
        "id": "hMDDhGkyv4jh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(le, open('labelencoder.pkl','wb'))\n",
        "pickle.dump(tokenizer, open('tokenizers.pkl','wb'))"
      ],
      "metadata": {
        "id": "B2OO2Avlvgtu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Membangun model dengan Neural Network**"
      ],
      "metadata": {
        "id": "ENf_iLr6wHFH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the model (Membuat Modeling)\n",
        "i = Input(shape=(input_shape,))\n",
        "x = Embedding(vocabulary+1,15)(i) # Layer Embedding\n",
        "x = LSTM(15, return_sequences=True)(x) # Layer Long Short Term Memory\n",
        "x = Flatten()(x) # Layer Flatten\n",
        "x = Dense(output_length, activation=\"softmax\")(x) # Layer Dense\n",
        "model  = Model(i,x)\n",
        "\n",
        "# Compiling the model (Kompilasi Model)\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "2H9-P9BbwAw1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization Plot Architecture Model (Visualisasi Plot Arsitektur Model)\n",
        "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "GYO1DW71wPdr",
        "outputId": "fef70bb5-4e8b-4cc5-cece-f14d12de88da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan Parameter Model\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhTAszciwQRm",
        "outputId": "7b246488-a63f-4b66-f3d1-8376648b3f8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 15)]              0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, 15, 15)            2700      \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 15, 15)            1860      \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 225)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 43)                9718      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,278\n",
            "Trainable params: 14,278\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model (Latih model data sampai n kali)\n",
        "train = model.fit(x_train, y_train, epochs=200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8BGFn5UwTe1",
        "outputId": "e2747ba7-39b7-4e12-82c5-fba5eff8fbf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "3/3 [==============================] - 7s 7ms/step - loss: 3.7578 - accuracy: 0.0421\n",
            "Epoch 2/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.7462 - accuracy: 0.0316\n",
            "Epoch 3/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.7329 - accuracy: 0.0421\n",
            "Epoch 4/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.7152 - accuracy: 0.0421\n",
            "Epoch 5/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.6909 - accuracy: 0.0737\n",
            "Epoch 6/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.6583 - accuracy: 0.1789\n",
            "Epoch 7/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 3.6175 - accuracy: 0.1789\n",
            "Epoch 8/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.5627 - accuracy: 0.1684\n",
            "Epoch 9/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.4991 - accuracy: 0.1684\n",
            "Epoch 10/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.4250 - accuracy: 0.1684\n",
            "Epoch 11/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.3302 - accuracy: 0.1684\n",
            "Epoch 12/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.2534 - accuracy: 0.1684\n",
            "Epoch 13/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.2012 - accuracy: 0.1684\n",
            "Epoch 14/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 3.1923 - accuracy: 0.1684\n",
            "Epoch 15/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.1900 - accuracy: 0.1684\n",
            "Epoch 16/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.1685 - accuracy: 0.1684\n",
            "Epoch 17/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.1411 - accuracy: 0.1684\n",
            "Epoch 18/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.1141 - accuracy: 0.1684\n",
            "Epoch 19/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.1005 - accuracy: 0.1684\n",
            "Epoch 20/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.0850 - accuracy: 0.1684\n",
            "Epoch 21/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.0649 - accuracy: 0.1789\n",
            "Epoch 22/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.0426 - accuracy: 0.1789\n",
            "Epoch 23/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.0202 - accuracy: 0.1789\n",
            "Epoch 24/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 3.0013 - accuracy: 0.1789\n",
            "Epoch 25/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 2.9791 - accuracy: 0.1789\n",
            "Epoch 26/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.9542 - accuracy: 0.1895\n",
            "Epoch 27/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.9313 - accuracy: 0.1895\n",
            "Epoch 28/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.9048 - accuracy: 0.2000\n",
            "Epoch 29/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 2.8811 - accuracy: 0.2000\n",
            "Epoch 30/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 2.8566 - accuracy: 0.2000\n",
            "Epoch 31/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.8303 - accuracy: 0.2105\n",
            "Epoch 32/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.8054 - accuracy: 0.2105\n",
            "Epoch 33/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.7803 - accuracy: 0.2105\n",
            "Epoch 34/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.7539 - accuracy: 0.2000\n",
            "Epoch 35/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.7282 - accuracy: 0.2000\n",
            "Epoch 36/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.7035 - accuracy: 0.2316\n",
            "Epoch 37/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.6763 - accuracy: 0.2632\n",
            "Epoch 38/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.6520 - accuracy: 0.2632\n",
            "Epoch 39/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.6251 - accuracy: 0.2632\n",
            "Epoch 40/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.6001 - accuracy: 0.2526\n",
            "Epoch 41/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.5750 - accuracy: 0.2526\n",
            "Epoch 42/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 2.5486 - accuracy: 0.2526\n",
            "Epoch 43/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.5231 - accuracy: 0.2526\n",
            "Epoch 44/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 2.4980 - accuracy: 0.2526\n",
            "Epoch 45/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.4717 - accuracy: 0.2632\n",
            "Epoch 46/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.4462 - accuracy: 0.2737\n",
            "Epoch 47/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.4193 - accuracy: 0.2737\n",
            "Epoch 48/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.3935 - accuracy: 0.2842\n",
            "Epoch 49/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.3688 - accuracy: 0.2842\n",
            "Epoch 50/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.3421 - accuracy: 0.2947\n",
            "Epoch 51/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.3160 - accuracy: 0.3053\n",
            "Epoch 52/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.2901 - accuracy: 0.3053\n",
            "Epoch 53/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.2631 - accuracy: 0.3158\n",
            "Epoch 54/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 2.2388 - accuracy: 0.3158\n",
            "Epoch 55/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.2118 - accuracy: 0.3158\n",
            "Epoch 56/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.1838 - accuracy: 0.3263\n",
            "Epoch 57/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.1582 - accuracy: 0.3263\n",
            "Epoch 58/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 2.1354 - accuracy: 0.3158\n",
            "Epoch 59/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.1076 - accuracy: 0.3158\n",
            "Epoch 60/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.0826 - accuracy: 0.3368\n",
            "Epoch 61/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 2.0595 - accuracy: 0.3474\n",
            "Epoch 62/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 2.0338 - accuracy: 0.3474\n",
            "Epoch 63/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 2.0097 - accuracy: 0.3474\n",
            "Epoch 64/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.9857 - accuracy: 0.3474\n",
            "Epoch 65/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.9600 - accuracy: 0.3579\n",
            "Epoch 66/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.9400 - accuracy: 0.3579\n",
            "Epoch 67/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.9154 - accuracy: 0.3684\n",
            "Epoch 68/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.8945 - accuracy: 0.3789\n",
            "Epoch 69/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.8683 - accuracy: 0.4000\n",
            "Epoch 70/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.8470 - accuracy: 0.3895\n",
            "Epoch 71/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.8245 - accuracy: 0.4105\n",
            "Epoch 72/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.8028 - accuracy: 0.4105\n",
            "Epoch 73/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.7793 - accuracy: 0.4421\n",
            "Epoch 74/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.7599 - accuracy: 0.4316\n",
            "Epoch 75/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.7379 - accuracy: 0.4421\n",
            "Epoch 76/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.7186 - accuracy: 0.4211\n",
            "Epoch 77/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.6929 - accuracy: 0.4316\n",
            "Epoch 78/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.6742 - accuracy: 0.4632\n",
            "Epoch 79/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.6547 - accuracy: 0.4842\n",
            "Epoch 80/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.6298 - accuracy: 0.5053\n",
            "Epoch 81/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.6109 - accuracy: 0.4947\n",
            "Epoch 82/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.5898 - accuracy: 0.5368\n",
            "Epoch 83/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.5711 - accuracy: 0.5368\n",
            "Epoch 84/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.5504 - accuracy: 0.5684\n",
            "Epoch 85/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.5265 - accuracy: 0.5684\n",
            "Epoch 86/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.5051 - accuracy: 0.5579\n",
            "Epoch 87/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.4858 - accuracy: 0.5789\n",
            "Epoch 88/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.4600 - accuracy: 0.5895\n",
            "Epoch 89/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.4397 - accuracy: 0.6000\n",
            "Epoch 90/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.4190 - accuracy: 0.6105\n",
            "Epoch 91/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.3966 - accuracy: 0.6211\n",
            "Epoch 92/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.3744 - accuracy: 0.6211\n",
            "Epoch 93/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.3510 - accuracy: 0.6526\n",
            "Epoch 94/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.3325 - accuracy: 0.6526\n",
            "Epoch 95/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.3089 - accuracy: 0.6632\n",
            "Epoch 96/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.2861 - accuracy: 0.6737\n",
            "Epoch 97/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.2686 - accuracy: 0.7158\n",
            "Epoch 98/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.2412 - accuracy: 0.7474\n",
            "Epoch 99/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.2178 - accuracy: 0.7263\n",
            "Epoch 100/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1942 - accuracy: 0.7474\n",
            "Epoch 101/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1734 - accuracy: 0.7579\n",
            "Epoch 102/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1484 - accuracy: 0.7789\n",
            "Epoch 103/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1295 - accuracy: 0.8000\n",
            "Epoch 104/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1064 - accuracy: 0.7789\n",
            "Epoch 105/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0841 - accuracy: 0.7789\n",
            "Epoch 106/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0626 - accuracy: 0.7895\n",
            "Epoch 107/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0414 - accuracy: 0.7789\n",
            "Epoch 108/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0210 - accuracy: 0.7789\n",
            "Epoch 109/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0009 - accuracy: 0.7895\n",
            "Epoch 110/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9799 - accuracy: 0.8105\n",
            "Epoch 111/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9594 - accuracy: 0.7895\n",
            "Epoch 112/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9384 - accuracy: 0.8000\n",
            "Epoch 113/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9228 - accuracy: 0.8211\n",
            "Epoch 114/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9011 - accuracy: 0.8105\n",
            "Epoch 115/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8820 - accuracy: 0.8105\n",
            "Epoch 116/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8657 - accuracy: 0.8211\n",
            "Epoch 117/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8455 - accuracy: 0.8105\n",
            "Epoch 118/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8286 - accuracy: 0.8211\n",
            "Epoch 119/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8139 - accuracy: 0.8526\n",
            "Epoch 120/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7969 - accuracy: 0.8632\n",
            "Epoch 121/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7822 - accuracy: 0.8105\n",
            "Epoch 122/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7671 - accuracy: 0.8211\n",
            "Epoch 123/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7488 - accuracy: 0.8316\n",
            "Epoch 124/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7373 - accuracy: 0.8526\n",
            "Epoch 125/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7215 - accuracy: 0.9053\n",
            "Epoch 126/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7079 - accuracy: 0.9053\n",
            "Epoch 127/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6936 - accuracy: 0.8842\n",
            "Epoch 128/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6779 - accuracy: 0.8632\n",
            "Epoch 129/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6673 - accuracy: 0.9053\n",
            "Epoch 130/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6521 - accuracy: 0.9158\n",
            "Epoch 131/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6419 - accuracy: 0.9158\n",
            "Epoch 132/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.6272 - accuracy: 0.9158\n",
            "Epoch 133/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6173 - accuracy: 0.9263\n",
            "Epoch 134/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6037 - accuracy: 0.9474\n",
            "Epoch 135/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5921 - accuracy: 0.9158\n",
            "Epoch 136/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5813 - accuracy: 0.9158\n",
            "Epoch 137/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5702 - accuracy: 0.9158\n",
            "Epoch 138/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5616 - accuracy: 0.9263\n",
            "Epoch 139/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5511 - accuracy: 0.9263\n",
            "Epoch 140/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5387 - accuracy: 0.9579\n",
            "Epoch 141/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5283 - accuracy: 0.9579\n",
            "Epoch 142/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5203 - accuracy: 0.9579\n",
            "Epoch 143/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.5102 - accuracy: 0.9579\n",
            "Epoch 144/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5007 - accuracy: 0.9474\n",
            "Epoch 145/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4912 - accuracy: 0.9474\n",
            "Epoch 146/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4839 - accuracy: 0.9474\n",
            "Epoch 147/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.4759 - accuracy: 0.9474\n",
            "Epoch 148/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4671 - accuracy: 0.9474\n",
            "Epoch 149/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4591 - accuracy: 0.9579\n",
            "Epoch 150/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.4510 - accuracy: 0.9474\n",
            "Epoch 151/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.4450 - accuracy: 0.9368\n",
            "Epoch 152/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.4364 - accuracy: 0.9579\n",
            "Epoch 153/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4284 - accuracy: 0.9474\n",
            "Epoch 154/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4211 - accuracy: 0.9684\n",
            "Epoch 155/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4138 - accuracy: 0.9579\n",
            "Epoch 156/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.4080 - accuracy: 0.9684\n",
            "Epoch 157/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.4010 - accuracy: 0.9684\n",
            "Epoch 158/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3951 - accuracy: 0.9579\n",
            "Epoch 159/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3886 - accuracy: 0.9579\n",
            "Epoch 160/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3814 - accuracy: 0.9684\n",
            "Epoch 161/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3754 - accuracy: 0.9684\n",
            "Epoch 162/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3691 - accuracy: 0.9684\n",
            "Epoch 163/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3647 - accuracy: 0.9684\n",
            "Epoch 164/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3581 - accuracy: 0.9684\n",
            "Epoch 165/200\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.3525 - accuracy: 0.9684\n",
            "Epoch 166/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.3481 - accuracy: 0.9684\n",
            "Epoch 167/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3435 - accuracy: 0.9684\n",
            "Epoch 168/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3372 - accuracy: 0.9684\n",
            "Epoch 169/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3324 - accuracy: 0.9684\n",
            "Epoch 170/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.3278 - accuracy: 0.9684\n",
            "Epoch 171/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3219 - accuracy: 0.9684\n",
            "Epoch 172/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3180 - accuracy: 0.9579\n",
            "Epoch 173/200\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.3137 - accuracy: 0.9684\n",
            "Epoch 174/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3079 - accuracy: 0.9684\n",
            "Epoch 175/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3054 - accuracy: 0.9579\n",
            "Epoch 176/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3000 - accuracy: 0.9579\n",
            "Epoch 177/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2957 - accuracy: 0.9579\n",
            "Epoch 178/200\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.2919 - accuracy: 0.9684\n",
            "Epoch 179/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2870 - accuracy: 0.9684\n",
            "Epoch 180/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2828 - accuracy: 0.9684\n",
            "Epoch 181/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2803 - accuracy: 0.9684\n",
            "Epoch 182/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2762 - accuracy: 0.9579\n",
            "Epoch 183/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2714 - accuracy: 0.9579\n",
            "Epoch 184/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2676 - accuracy: 0.9684\n",
            "Epoch 185/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2641 - accuracy: 0.9684\n",
            "Epoch 186/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2609 - accuracy: 0.9684\n",
            "Epoch 187/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2575 - accuracy: 0.9684\n",
            "Epoch 188/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2536 - accuracy: 0.9684\n",
            "Epoch 189/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2503 - accuracy: 0.9684\n",
            "Epoch 190/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2468 - accuracy: 0.9684\n",
            "Epoch 191/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.2438 - accuracy: 0.9684\n",
            "Epoch 192/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.2411 - accuracy: 0.9684\n",
            "Epoch 193/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2374 - accuracy: 0.9684\n",
            "Epoch 194/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2341 - accuracy: 0.9684\n",
            "Epoch 195/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2323 - accuracy: 0.9684\n",
            "Epoch 196/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2290 - accuracy: 0.9684\n",
            "Epoch 197/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2251 - accuracy: 0.9684\n",
            "Epoch 198/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2229 - accuracy: 0.9579\n",
            "Epoch 199/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.2198 - accuracy: 0.9579\n",
            "Epoch 200/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2163 - accuracy: 0.9684\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Analysis**"
      ],
      "metadata": {
        "id": "NZPNg-mCwb09"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting model Accuracy and Loss (Visualisasi Plot Hasil Akurasi dan Loss)\n",
        "# Plot Akurasi\n",
        "plt.figure(figsize=(14,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(train.history['accuracy'],label='Training Set Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Accuracy')\n",
        "# Plot Loss\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(train.history['loss'],label='Training Set Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "Z2DlkpDYwaww",
        "outputId": "2f2894af-e8e4-43d0-d584-a4cea2a25040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Percobaan**"
      ],
      "metadata": {
        "id": "NXDPfvZd1zDG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat Input Chat\n",
        "while True:\n",
        "  texts_p = []\n",
        "  prediction_input = input('👨‍🦰 Kamu : ')\n",
        "  \n",
        "  # Menghapus punktuasi dan konversi ke huruf kecil\n",
        "  prediciton_input = [letters.lower() for letters in prediction_input if letters not in string.punctuation]\n",
        "  prediction_input = ''.join(prediction_input)\n",
        "  texts_p.append(prediction_input)\n",
        "\n",
        "  # Tokenisasi dan Padding\n",
        "  prediction_input = tokenizer.texts_to_sequences(texts_p)\n",
        "  prediction_input = np.array(prediction_input).reshape(-1)\n",
        "  prediction_input = pad_sequences([prediction_input],input_shape)\n",
        "\n",
        "  # Mendapatkan hasil keluaran pada model \n",
        "  output = model.predict(prediction_input)\n",
        "  output = output.argmax()\n",
        "\n",
        "  # Menemukan respon sesuai data tag dan memainkan voice bot\n",
        "  response_tag = le.inverse_transform([output])[0]\n",
        "  print(\"🤖 KadekBot : \", random.choice(responses[response_tag]))\n",
        "  # tts = gTTS(random.choice(responses[response_tag]), lang='id')\n",
        "  # Simpan model voice bot ke dalam Google Drive\n",
        "  # tts.save('/content/Model/Kadekbot.wav')\n",
        "  time.sleep(0.08)\n",
        "  # Load model voice bot from Google Drive\n",
        "  # ipd.display(ipd.Audio('/content/Model/Kadekbot.wav', autoplay=False))\n",
        "  print(\"=\"*60 + \"\\n\")\n",
        "  # Tambahkan respon 'goodbye' agar bot bisa berhenti\n",
        "  if response_tag == \"goodbye\":\n",
        "    break"
      ],
      "metadata": {
        "id": "AdhApHSQ1sdz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fb15c66-7442-40c7-edd9-ca246da6bf97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "👨‍🦰 Kamu : hai\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "🤖 KadekBot :  Halo! Aku Dinusbot yang akan menjawab pertanyaanmu seputar UDINUS, apa yang ingin kamu tanyakan?\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : UDINUS\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "🤖 KadekBot :  Universitas Dian Nuswantoro adalah salah satu perguruan tinggi swasta berakreditasi institusi A yang berada di Semarang, Indonesia. Berdiri pada tahun 1990. Rektor pada tahun 2010 adalah Prof. Dr. Ir. Edi Noersasongko M. Kom.\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Sejarah udinus\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "🤖 KadekBot :  Pada tahun 1986, didirikan sebuah lembaga kursus komputer IMKA yang berlokasi di kota Semarang. Karena kegigihannya dalam membuka dan menciptakan peluang pasar serta ketahanannya dalam menghadapi pelbagai rintangan, IMKA berhasil tumbuh dan berkembang serta menyebar ke beberapa kota besar di Pulau Jawa, seperti Semarang, Surakarta, Yogyakarta, Surabaya, Bandung dan Jakarta. Selengkapnya seputar sejarah UDINUS di https://dinus.ac.id/about/52/history-dinus\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Dimana parkir mobil dan motor di udinus?\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "🤖 KadekBot :  Untuk semantara ini, area parkir adalah di - Depan gedung B (untuk kendaraan dosen dan staff) - Depan gedung E (untuk mobil dan karyawan TVku) dan sebagian untuk kendaraan mahasiswa - Sebrang gedung E ( untuk mobil ) - Depan gedung G (untuk mobil) - Belakang gedung G (untuk motor) - Sebelah poliklinik (untuk mobil).\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Dimana udinus?\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "🤖 KadekBot :  Universitas Dian Nuswantoro (Universitas Dinus) terletak di pusat kota Semarang, Jawa Tengah, Indonesia. Terletak di dekat bundaran Tugu Muda, Jl. Imam Bonjol 207 dan Jl. Nakula I no 5-11 Semarang tepatnya. Dapat diakses dengan mudah baik dengan kendaraan umum maupun kendaraan pribadi. Dibutuhkan sekitar 12 menit dari Bandara Ahmad Yani. Terletak di area seluas 4,5 Ha, kampus ini dikelilingi oleh area perkantoran, pendidikan, gedung komersial dan perumahan.\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Fasilitas udinus\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "🤖 KadekBot :  UDINUS memiliki berbagai fasilitas yang diantaranya seperti lab fotografi, perpustakaan, sistem informasi, ruang rapat H.1, kafetaria, klinik, masjid, pusat kegiatan mahasiswa, Turnitin, ruang kelas nyaman, internet cepat, laboratorium, bus UDINUS, rooftop garden, beasiswa, lapangan basket, kalender akademik dinus, tenda kewirausahaan dinus, unit kegiatan dan organisasi mahasiswa, akses jurnal berlangganan, wall climbing, lab kesehatan, dinus tax center, lembaga bahasa ILCS dan CFLT, peluang studi lanjutan luar negeri, lab industri, ujian online, jadwal online, dan ruang aula. Selengkapnya di https://dinus.ac.id/facility\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Jika saya pergi berobat ke poliklinik udinus, perlukah saya membayar untuk pelayanan & obatnya?\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "🤖 KadekBot :  Tidak, dikarenakan pembayaran poliklinik sudah masuk dalam pembayaran semesteran. \n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Ada berapa jenis poli yang ada di poliklinik udinus?\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "🤖 KadekBot :  Poli Umum dan Poli Gigi serta Poli Psikologi.\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Apa saja jenis-jenis beasiswa yang dikelola udinus?\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "🤖 KadekBot :  Melengkapi data di SiAdin Mahasiswa di menu 'e-Ticket KTM/Heregistrasi'. Isi semua kelengkapan data seperti upload file Ijazah, KTP, KK (cek informasi syarat dan ketentuan upload file). Selanjutnya jika sudah berhasil upload semua file silahkan klik 'Download Tiket Pengambilan KTM ke BIAK', cetak dan berikan ke BIAK di Gd. A lt. 1\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Ada berapa macam kegiatan peneliitian/karya ilmiah yang dapat diikut oleh mahasiswa udinus?\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "🤖 KadekBot :  Silakan cek di website http://lppm.dinus.ac.id/index.php menu statistik.\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : Bagaimana menjadi member career center udinus?\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "🤖 KadekBot :  Untuk menjadi member di Career Center UDINUS sangatlah mudah. Silahkan registrasi melalui website https://cc.dinus.ac.id - pilih menu Jobfair - Registrasi - Pencari Kerja.\n",
            "============================================================\n",
            "\n",
            "👨‍🦰 Kamu : dah\n",
            "1/1 [==============================] - 0s 16ms/step\n",
            "🤖 KadekBot :  Bye!\n",
            "============================================================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Simpan model dalam bentuk format file .h5 atau .pkl (pickle)\n",
        "model.save('/content/Model/chat_model.h5')\n",
        "\n",
        "print('Model Created Successfully!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhTpyFzY2lwx",
        "outputId": "88bd9f76-2d05-4fcc-a21e-e1a4e02743bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Created Successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Kesimpulan**\n",
        "Setelah melakukan proses training dengan model RNN-LSTM, didapatkan hasil yang memuaskan, dengan hasil metrics accuracy sebesar 0.9684 dan loss sebesar 0.2163 dengan epochs sebanyak 200. Selanjutnya, pada tahap percobaan simulasi chatbot di atas menunjukkan, bahwa 10 pertanyaan yang diuji coba, 8 dari 10 pertanyaan dapat dijawab dengan benar oleh DinusBot dan 2 pertanyaan sisanya tidak dapat dijawab dengan benar oleh DinusBot. Hal ini dikarenakan kurang banyak dan beragamnya dataset, sehingga menyebabkan ketidaktepatan DinusBot dalam menjawab pertanyaan/pernyataan dari si pengguna. Oleh karena itu, diperlukan adanya pengembangan dataset yang digunakan agar kemampuan DinusBot dalam merespon lebih baik lagi."
      ],
      "metadata": {
        "id": "USCL7wTaKVRp"
      }
    }
  ]
}