{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRSlxT4q5EHJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from google.colab import files\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Upload file dari PC\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "gaeeT_rKVXTW",
        "outputId": "8ab43ba4-3aef-4c39-aaab-df5e386f8894"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-09d94cbc-2567-4f8f-bb57-ee0d6c0dbad4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-09d94cbc-2567-4f8f-bb57-ee0d6c0dbad4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving data real bulanan 2019-2023 supremo.csv to data real bulanan 2019-2023 supremo.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fRF6mEW5QbK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3205b015-769e-4c63-ba8d-7e92c380473f"
      },
      "source": [
        "df = pd.read_csv('data real bulanan 2019-2023 supremo.csv', delimiter=';')\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Nomer  Tahun   Bulan  Produksi\n",
              "0      1   2019  Jan-19    170252\n",
              "1      2   2019  Feb-19    323468\n",
              "2      3   2019  Mar-19    616344\n",
              "3      4   2019  Apr-19    503524\n",
              "4      5   2019  May-19    277856"
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1,\n        \"max\": 60,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          1,\n          6,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2023,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2020,\n          2023,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 60,\n        \"samples\": [\n          \"Jan-19\",\n          \"Jun-19\",\n          \"Jan-22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162557,\n        \"min\": 90528,\n        \"max\": 734748,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          170252,\n          104272,\n          530084\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9, 6))\n",
        "plt.plot(df.index, df['Produksi'], color='blue', marker='o', markersize=2, linewidth=2, alpha=0.7)\n",
        "plt.title('Data Produksi')\n",
        "plt.xlabel('Bulan')\n",
        "plt.ylabel('Produksi')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IctHV7x_M-vw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "386d2cd8-edb3-4de3-c1ae-8bd141540ace"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changed period to 12 for monthly data\n",
        "results = seasonal_decompose(df['Produksi'], model='additive', period=12)\n",
        "results.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "BuK5VqtkV064",
        "outputId": "0fb36b6b-3882-40d9-8ea0-6e6234b7ff72"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Seed Untuk Reproduksi Data\n",
        "# Set random seeds di semua library\n",
        "seed_value = 42\n",
        "random.seed(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "tf.random.set_seed(seed_value)"
      ],
      "metadata": {
        "id": "OGF2sq0XWQ6K"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NoWcZEK6rPb"
      },
      "source": [
        "# Split data into training and testing sets\n",
        "train = df.iloc[:54].copy()\n",
        "test = df.iloc[54:].copy()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepocessing Data"
      ],
      "metadata": {
        "id": "86lxwp6PWwNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepocessing Data with MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaled_train = scaler.fit_transform(train[['Produksi']])\n",
        "scaled_test = scaler.transform(test[['Produksi']])\n",
        "\n",
        "print(\"=== Data Test ===\")\n",
        "print(\"Train Original:\", train['Produksi'].head(3).values)\n",
        "print(\"Train Scaled:\", scaled_train[:3].flatten())"
      ],
      "metadata": {
        "id": "xdvMcAEm8b4q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35fb45d6-762c-48c9-ab91-c5b8729f2220"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Data Test ===\n",
            "Train Original: [170252 323468 616344]\n",
            "Train Scaled: [0.12375276 0.36158455 0.81620564]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model LSTM"
      ],
      "metadata": {
        "id": "NTKpVM7VXIQG"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqyTKCVt69Z9"
      },
      "source": [
        "# define generator\n",
        "n_input = 12\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip6y5Ene7OM7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7c1a725-0e8a-4010-c1d2-3deffa3c22c4"
      },
      "source": [
        "X,y = generator[0]\n",
        "print(f'Given the Array: \\n{X.flatten()}')\n",
        "print(f'Predict this y: \\n {y}')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Given the Array: \n",
            "[0.12375276 0.36158455 0.81620564 0.64107913 0.29078265 0.02133433\n",
            " 0.55775977 0.45020645 0.2241284  0.13337059 0.51362578 0.33075036]\n",
            "Predict this y: \n",
            " [[0.2472944]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQyGmvsi8HyR",
        "outputId": "32c5fae7-91ac-41f4-e354-28c06cae55de"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 12, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jm-T4H4I7UaV"
      },
      "source": [
        "# We do the same thing, but now instead for 1 months\n",
        "n_input = 11\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJa-wFQf7jw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5553557-84d4-45ec-cd23-0a1f3bf03235"
      },
      "source": [
        "# Set seeds for reproducibility\n",
        "np.random.seed(42)\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(79, activation='relu', input_shape=(n_input, n_features)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "7i4ucUNv7lbk",
        "outputId": "b6654fcf-014b-4d1e-cd38-866806e400e4"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m79\u001b[0m)             │        \u001b[38;5;34m25,596\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m80\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">25,596</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m25,676\u001b[0m (100.30 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,676</span> (100.30 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m25,676\u001b[0m (100.30 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,676</span> (100.30 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8yqUZaz7nwp",
        "outputId": "ec81f1b5-0a9f-4b55-d08d-53999ca751cd"
      },
      "source": [
        "# fit model\n",
        "history = model.fit(generator,epochs=93)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - loss: 0.1179\n",
            "Epoch 2/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0787\n",
            "Epoch 3/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0537\n",
            "Epoch 4/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0529\n",
            "Epoch 5/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0632\n",
            "Epoch 6/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0692\n",
            "Epoch 7/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0787\n",
            "Epoch 8/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0658\n",
            "Epoch 9/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0709\n",
            "Epoch 10/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0676\n",
            "Epoch 11/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0842\n",
            "Epoch 12/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0668\n",
            "Epoch 13/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0925\n",
            "Epoch 14/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0445\n",
            "Epoch 15/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0566\n",
            "Epoch 16/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0556\n",
            "Epoch 17/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0809\n",
            "Epoch 18/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0653\n",
            "Epoch 19/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0648\n",
            "Epoch 20/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0733\n",
            "Epoch 21/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0617\n",
            "Epoch 22/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0613\n",
            "Epoch 23/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0735\n",
            "Epoch 24/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0771\n",
            "Epoch 25/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0816\n",
            "Epoch 26/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0874\n",
            "Epoch 27/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0793\n",
            "Epoch 28/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0595\n",
            "Epoch 29/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - loss: 0.0487\n",
            "Epoch 30/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0571\n",
            "Epoch 31/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0670\n",
            "Epoch 32/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0679\n",
            "Epoch 33/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0709\n",
            "Epoch 34/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0768\n",
            "Epoch 35/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0520\n",
            "Epoch 36/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0635\n",
            "Epoch 37/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0630\n",
            "Epoch 38/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0756\n",
            "Epoch 39/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0499\n",
            "Epoch 40/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0582\n",
            "Epoch 41/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0630\n",
            "Epoch 42/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0509\n",
            "Epoch 43/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0417\n",
            "Epoch 44/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0678\n",
            "Epoch 45/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0577\n",
            "Epoch 46/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0573\n",
            "Epoch 47/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0494\n",
            "Epoch 48/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0598\n",
            "Epoch 49/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0585\n",
            "Epoch 50/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0659\n",
            "Epoch 51/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0517\n",
            "Epoch 52/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0680\n",
            "Epoch 53/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0361\n",
            "Epoch 54/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 0.0600\n",
            "Epoch 55/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0745\n",
            "Epoch 56/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0463\n",
            "Epoch 57/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0389\n",
            "Epoch 58/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0529\n",
            "Epoch 59/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0505\n",
            "Epoch 60/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0733\n",
            "Epoch 61/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0435\n",
            "Epoch 62/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0687\n",
            "Epoch 63/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0360\n",
            "Epoch 64/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0371\n",
            "Epoch 65/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0750\n",
            "Epoch 66/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0552\n",
            "Epoch 67/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0344\n",
            "Epoch 68/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0529\n",
            "Epoch 69/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0496\n",
            "Epoch 70/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0468\n",
            "Epoch 71/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0675\n",
            "Epoch 72/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0364\n",
            "Epoch 73/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0433\n",
            "Epoch 74/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0608\n",
            "Epoch 75/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0447\n",
            "Epoch 76/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0377\n",
            "Epoch 77/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0369\n",
            "Epoch 78/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0391\n",
            "Epoch 79/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0451\n",
            "Epoch 80/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0649\n",
            "Epoch 81/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0560\n",
            "Epoch 82/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0629\n",
            "Epoch 83/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 0.0459\n",
            "Epoch 84/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0542\n",
            "Epoch 85/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0509\n",
            "Epoch 86/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0518\n",
            "Epoch 87/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0434\n",
            "Epoch 88/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0504\n",
            "Epoch 89/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0520\n",
            "Epoch 90/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0414\n",
            "Epoch 91/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0431\n",
            "Epoch 92/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0358\n",
            "Epoch 93/93\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "OjDsOKR67rs8",
        "outputId": "cc1807bb-40a0-4369-d973-3577b93e36bd"
      },
      "source": [
        "loss_per_epoch = model.history.history['loss']\n",
        "plt.plot(range(len(loss_per_epoch)),loss_per_epoch)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b1b364f2850>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6h-fell70_S"
      },
      "source": [
        "last_train_batch = scaled_train[-11:]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwwv8O-p79bN"
      },
      "source": [
        "last_train_batch = last_train_batch.reshape((1, n_input, n_features))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-4WD4t78MpJ",
        "outputId": "f59690c1-e570-4dcc-dd68-46140a4a09e4"
      },
      "source": [
        "model.predict(last_train_batch)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.5497148]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbojFADE8StA",
        "outputId": "8728b689-9456-4f08-9cae-ffc9ce6c4d70"
      },
      "source": [
        "scaled_test[0]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.55599019])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MP-5Wez8jbY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd51cf42-2480-4443-e864-39d09f156251"
      },
      "source": [
        "test_predictions = []\n",
        "\n",
        "first_eval_batch = scaled_train[-n_input:]\n",
        "current_batch = first_eval_batch.reshape((1, n_input, n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "\n",
        "    # get the prediction value for the first batch\n",
        "    current_pred = model.predict(current_batch)[0]\n",
        "\n",
        "    # append the prediction into the array\n",
        "    test_predictions.append(current_pred)\n",
        "\n",
        "    # use the prediction to update the batch and remove the first value\n",
        "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mhvFuEy8sB7",
        "outputId": "842b6d86-a97a-48c1-fd3e-1d2e52c61e47"
      },
      "source": [
        "test_predictions"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.5497148], dtype=float32),\n",
              " array([0.37593487], dtype=float32),\n",
              " array([0.35805827], dtype=float32),\n",
              " array([0.44880283], dtype=float32),\n",
              " array([0.6524668], dtype=float32),\n",
              " array([0.77487695], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YYSqV10E9hlO",
        "outputId": "91686219-8fd9-4f47-e569-deb2064245ac"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Nomer  Tahun   Bulan  Produksi\n",
              "54     55   2023  Jul-23    448708\n",
              "55     56   2023  Aug-23    412512\n",
              "56     57   2023  Sep-23    299914\n",
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              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 55,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          55,\n          56,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2023,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Jul-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135373,\n        \"min\": 299914,\n        \"max\": 686476,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          448708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHHzjNDK9EJZ"
      },
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)\n",
        "test['Predictions'] = true_predictions"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the absolute difference between 'Produksi' and 'Predictions'\n",
        "test['Error'] = abs((test['Produksi'] - test['Predictions'])/test['Produksi'])*100\n",
        "\n",
        "# Display the updated DataFrame with the new 'Error' column\n",
        "test\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "qX-ypp5gqywe",
        "outputId": "cdc22aec-ab29-45b4-c03c-aee977e06375"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Nomer  Tahun   Bulan  Produksi    Predictions      Error\n",
              "54     55   2023  Jul-23    448708  444665.270837   0.900971\n",
              "55     56   2023  Aug-23    412512  332712.761340  19.344707\n",
              "56     57   2023  Sep-23    299914  321196.301142   7.096135\n",
              "57     58   2023  Oct-23    368213  379655.758362   3.107646\n",
              "58     59   2023  Nov-23    523068  510860.145138   2.333894\n",
              "59     60   2023  Dec-23    686476  589719.230128  14.094705"
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "    <div id=\"df-1b9e67cb-afbd-47ef-a3e4-379ed58c76c7\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1b9e67cb-afbd-47ef-a3e4-379ed58c76c7')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-1b9e67cb-afbd-47ef-a3e4-379ed58c76c7 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_87dfcfd0-627c-4dff-85b3-4bcc97c782da\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_87dfcfd0-627c-4dff-85b3-4bcc97c782da button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 55,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          55,\n          56,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2023,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Jul-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135373,\n        \"min\": 299914,\n        \"max\": 686476,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          448708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105925.46798775719,\n        \"min\": 321196.30114245415,\n        \"max\": 589719.2301278114,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          444665.27083683014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.387707142950213,\n        \"min\": 0.9009710464644849,\n        \"max\": 19.34470722305725,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9009710464644849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ... (Your existing plotting code) ...\n",
        "\n",
        "# Calculate MAPE (assuming you already have y_true and y_pred)\n",
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "y_true = test['Produksi'].values\n",
        "y_pred = test['Predictions'].values\n",
        "\n",
        "mape_final = mean_absolute_percentage_error(y_true, y_pred) #Calculate and assign mape_final\n",
        "\n",
        "# ... (Rest of your plotting code) ...\n",
        "\n",
        "mape_text = f\"MAPE: {mape_final:.2f}%\" #Now mape_final is defined\n",
        "\n",
        "plt.annotate(mape_text,\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "\n",
        "\n",
        "plt.figure(figsize=(14, 7))\n",
        "\n",
        "# Plot data aktual\n",
        "plt.plot(test.index, test['Produksi'],\n",
        "         label='Data Aktual',\n",
        "         color='blue',\n",
        "         marker='o',\n",
        "         markersize=5,\n",
        "         linewidth=2,\n",
        "         alpha=0.7)\n",
        "\n",
        "# Plot prediksi\n",
        "plt.plot(test.index, test['Predictions'],\n",
        "         label='Prediksi Model',\n",
        "         color='red',\n",
        "         linestyle='--',\n",
        "         marker='x',\n",
        "         markersize=6,\n",
        "         linewidth=2)\n",
        "\n",
        "# Tambahkan detail grafik\n",
        "plt.title('Perbandingan Data Aktual vs Prediksi Model', fontsize=16, pad=20)\n",
        "plt.xlabel('Tanggal', fontsize=12)\n",
        "plt.ylabel('Produksi', fontsize=12)\n",
        "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
        "plt.legend(fontsize=12)\n",
        "\n",
        "# Rotasi label tanggal jika terlalu padat\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Tambahkan anotasi MAPE\n",
        "mape_text = f\"MAPE: {mape_final:.2f}%\"\n",
        "plt.annotate(mape_text,\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "\n",
        "# Highlight area error\n",
        "plt.fill_between(test.index,\n",
        "                 test['Produksi'],\n",
        "                 test['Predictions'],\n",
        "                 color='orange',\n",
        "                 alpha=0.1,\n",
        "                 label='Error')\n",
        "\n",
        "plt.tight_layout()  # Adjust layout\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "id": "fjvREAUvjxQw",
        "outputId": "2b3ffd6c-ce96-4c43-fc05-efb3ca8c63c4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCsYjzge9OVe",
        "outputId": "b597ec9b-8a70-4c32-a59b-52d6b77c16b6"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "rmse=sqrt(mean_squared_error(test['Produksi'],test['Predictions']))\n",
        "print(f\"RMSE Model LSTM: {rmse:.2f}\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE Model LSTM: 52407.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "# Gunakan nilai sebenarnya dari data test dan prediksi model:\n",
        "y_true = test['Produksi'].values\n",
        "y_pred = test['Predictions'].values\n",
        "\n",
        "mape = mean_absolute_percentage_error(y_true, y_pred)\n",
        "print(f\"MAPE Model LSTM: {mape:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0y8tGeJgLFR8",
        "outputId": "60be8587-676d-4695-b29a-87ba36372589"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE Model LSTM: 7.81%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: evaluas mae-nya juga\n",
        "\n",
        "# Evaluasi MAE (Mean Absolute Error)\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mae = mean_absolute_error(y_true, y_pred)\n",
        "print(f\"MAE Model LSTM: {mae:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMHlVmMH-vyM",
        "outputId": "5a226343-5a9b-4b57-af42-ee05d30a3e44"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE Model LSTM: 37588.61\n"
          ]
        }
      ]
    },
    {
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime # Import for handling dates\n",
        "import matplotlib.dates as mdates # Import for date formatting in plots\n",
        "\n",
        "# Atur opsi pandas untuk menampilkan semua baris\n",
        "pd.set_option('display.max_rows', None)\n",
        "pd.set_option('display.max_columns', None)\n",
        "\n",
        "\n",
        "# --- Forecasting 3 months into the future ---\n",
        "\n",
        "n_future_months = 3 # Define how many future months to forecast\n",
        "\n",
        "# Combine scaled_train and scaled_test to get the full historical data\n",
        "# This is the sequence the model will use to start forecasting from\n",
        "full_scaled_data = np.concatenate((scaled_train, scaled_test))\n",
        "\n",
        "# Get the last n_input values from the full_scaled_data\n",
        "# This forms the initial batch for forecasting\n",
        "current_batch_forecast = full_scaled_data[-n_input:].reshape((1, n_input, 1))\n",
        "\n",
        "forecasted_values = []\n",
        "\n",
        "for i in range(n_future_months):\n",
        "    # Predict the next step\n",
        "    # Change final_model to model\n",
        "    current_pred_forecast = model.predict(current_batch_forecast, verbose=0)[0]\n",
        "    forecasted_values.append(current_pred_forecast)\n",
        "\n",
        "    # Update the batch by sliding the window forward\n",
        "    # This takes the last n_input-1 values and appends the new prediction\n",
        "    current_batch_forecast = np.append(current_batch_forecast[:,1:,:], [[current_pred_forecast]], axis=1)\n",
        "\n",
        "# Inverse transform the forecasted values\n",
        "forecasted_values_actual_scale = scaler.inverse_transform(forecasted_values)\n",
        "\n",
        "# Construct the date for the last entry in the original df\n",
        "last_year = df['Tahun'].iloc[-1]\n",
        "last_month = df['Bulan'].iloc[-1]\n",
        "# Create a datetime object for the last day of the last month in the data\n",
        "last_date = pd.to_datetime(f'{last_year}-{last_month}-01') + pd.offsets.MonthEnd(0)\n",
        "\n",
        "\n",
        "future_dates = [last_date + pd.DateOffset(months=i) for i in range(1, n_future_months + 1)]\n",
        "\n",
        "# Create a DataFrame for the forecast\n",
        "forecast_df = pd.DataFrame(index=future_dates, data={'Produksi_Forecast': forecasted_values_actual_scale.flatten()})\n",
        "\n",
        "print(\"\\n=== Hasil Peramalan 3 Bulan Ke Depan ===\")\n",
        "print(forecast_df)\n",
        "\n",
        "# --- Update the final plot to include the forecast ---\n",
        "plt.figure(figsize=(16, 8)) # Make the plot larger to accommodate forecast\n",
        "\n",
        "df_dates = [pd.to_datetime(f\"{year}-{month}-01\") for year, month in zip(df['Tahun'], df['Bulan'])]\n",
        "df.index = df_dates\n",
        "\n",
        "train_dates = df.index[:len(train)]\n",
        "test_dates = df.index[len(train):len(train)+len(test)]\n",
        "\n",
        "plt.plot(train_dates, train['Produksi'], label='Data Latih Aktual', color='green', alpha=0.7)\n",
        "\n",
        "# Plot historical test data\n",
        "plt.plot(test_dates, test['Produksi'], label='Data Uji Aktual', color='blue', marker='o', markersize=4, linewidth=2, alpha=0.7)\n",
        "\n",
        "# Plot predictions for the test set\n",
        "# test['Predictions'] was added using the test DataFrame index, which should align with test_dates\n",
        "plt.plot(test_dates, test['Predictions'], label='Prediksi Data Uji', color='red', linestyle='--', marker='x', markersize=5, linewidth=2)\n",
        "\n",
        "# Plot the forecasted values\n",
        "plt.plot(forecast_df.index, forecast_df['Produksi_Forecast'], label=f'Peramalan {n_future_months} Bulan', color='purple', linestyle='-.', marker='s', markersize=5, linewidth=2)\n",
        "\n",
        "plt.title(f'Data Aktual, Prediksi Model, dan Peramalan {n_future_months} Bulan', fontsize=18, pad=20)\n",
        "plt.xlabel('Bulan dan Tahun', fontsize=14)\n",
        "plt.ylabel('Produksi', fontsize=14)\n",
        "plt.grid(True, which='both', linestyle='--', linewidth=0.7)\n",
        "plt.legend(fontsize=12)\n",
        "\n",
        "# Adjust x-axis ticks for better readability, covering all data including forecast\n",
        "ax = plt.gca()\n",
        "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\n",
        "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=6)) # Show ticks every 6 months\n",
        "\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# --- Optional: Display combined actual, prediction, and forecast data ---\n",
        "# Ensure indices are aligned for concatenation\n",
        "# Use the updated df with date index\n",
        "combined_data = pd.concat([df[['Produksi']], test[['Predictions']].rename(columns={'Predictions': 'Produksi_Prediksi_Uji'}), forecast_df])\n",
        "print(\"\\n=== Data Gabungan (Aktual, Prediksi Uji, Peramalan) ===\")\n",
        "print(combined_data)\n",
        "\n",
        "# Reset pandas options to default after displaying the full table (optional)\n",
        "pd.reset_option('display.max_rows')\n",
        "pd.reset_option('display.max_columns')"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tgWxK36phEji",
        "outputId": "5773e04e-f75e-4229-bf12-bb0f75da35f6"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Hasil Peramalan 3 Bulan Ke Depan ===\n",
            "                     Produksi_Forecast\n",
            "2024-01-31 01:00:00      468639.453767\n",
            "2024-02-29 01:00:00      330657.750189\n",
            "2024-03-31 01:00:00      276479.092619\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Data Gabungan (Aktual, Prediksi Uji, Peramalan) ===\n",
            "                     Produksi  Produksi_Prediksi_Uji  Produksi_Forecast\n",
            "2019-01-19 01:00:00  170252.0                    NaN                NaN\n",
            "2019-02-19 01:00:00  323468.0                    NaN                NaN\n",
            "2019-03-19 01:00:00  616344.0                    NaN                NaN\n",
            "2019-04-19 01:00:00  503524.0                    NaN                NaN\n",
            "2019-05-19 01:00:00  277856.0                    NaN                NaN\n",
            "2019-06-19 01:00:00  104272.0                    NaN                NaN\n",
            "2019-07-19 01:00:00  449848.0                    NaN                NaN\n",
            "2019-08-19 01:00:00  380560.0                    NaN                NaN\n",
            "2019-09-19 01:00:00  234916.0                    NaN                NaN\n",
            "2019-10-19 01:00:00  176448.0                    NaN                NaN\n",
            "2019-11-19 01:00:00  421416.0                    NaN                NaN\n",
            "2019-12-19 01:00:00  303604.0                    NaN                NaN\n",
            "2020-01-20 01:00:00  249840.0                    NaN                NaN\n",
            "2020-02-20 01:00:00  335684.0                    NaN                NaN\n",
            "2020-03-20 01:00:00  526548.0                    NaN                NaN\n",
            "2020-04-20 01:00:00  408456.0                    NaN                NaN\n",
            "2020-05-20 01:00:00  212064.0                    NaN                NaN\n",
            "2020-06-20 01:00:00  324152.0                    NaN                NaN\n",
            "2020-07-20 01:00:00  435356.0                    NaN                NaN\n",
            "2020-08-20 01:00:00  113888.0                    NaN                NaN\n",
            "2020-09-20 01:00:00  266652.0                    NaN                NaN\n",
            "2020-10-20 01:00:00  375152.0                    NaN                NaN\n",
            "2020-11-20 01:00:00  670988.0                    NaN                NaN\n",
            "2020-12-20 01:00:00  287864.0                    NaN                NaN\n",
            "2021-01-21 01:00:00  462136.0                    NaN                NaN\n",
            "2021-02-21 01:00:00  619136.0                    NaN                NaN\n",
            "2021-03-21 01:00:00  364564.0                    NaN                NaN\n",
            "2021-04-21 01:00:00  389484.0                    NaN                NaN\n",
            "2021-05-21 01:00:00   90528.0                    NaN                NaN\n",
            "2021-06-21 01:00:00  147216.0                    NaN                NaN\n",
            "2021-07-21 01:00:00  289676.0                    NaN                NaN\n",
            "2021-08-21 01:00:00  173664.0                    NaN                NaN\n",
            "2021-09-21 01:00:00  161092.0                    NaN                NaN\n",
            "2021-10-21 01:00:00  330096.0                    NaN                NaN\n",
            "2021-11-21 01:00:00  507136.0                    NaN                NaN\n",
            "2021-12-21 01:00:00  421612.0                    NaN                NaN\n",
            "2022-01-22 01:00:00  530084.0                    NaN                NaN\n",
            "2022-02-22 01:00:00  665968.0                    NaN                NaN\n",
            "2022-03-22 01:00:00  262580.0                    NaN                NaN\n",
            "2022-04-22 01:00:00  154440.0                    NaN                NaN\n",
            "2022-05-22 01:00:00  448988.0                    NaN                NaN\n",
            "2022-06-22 01:00:00  591824.0                    NaN                NaN\n",
            "2022-07-22 01:00:00  469864.0                    NaN                NaN\n",
            "2022-08-22 01:00:00  488904.0                    NaN                NaN\n",
            "2022-09-22 01:00:00  174284.0                    NaN                NaN\n",
            "2022-10-22 01:00:00  317624.0                    NaN                NaN\n",
            "2022-11-22 01:00:00  433180.0                    NaN                NaN\n",
            "2022-12-22 01:00:00  734748.0                    NaN                NaN\n",
            "2023-01-23 01:00:00  658516.0                    NaN                NaN\n",
            "2023-02-23 01:00:00  470784.0                    NaN                NaN\n",
            "2023-03-23 01:00:00  340776.0                    NaN                NaN\n",
            "2023-04-23 01:00:00  154320.0                    NaN                NaN\n",
            "2023-05-23 01:00:00  522792.0                    NaN                NaN\n",
            "2023-06-23 01:00:00  463184.0                    NaN                NaN\n",
            "2023-07-23 01:00:00  448708.0                    NaN                NaN\n",
            "2023-08-23 01:00:00  412512.0                    NaN                NaN\n",
            "2023-09-23 01:00:00  299914.0                    NaN                NaN\n",
            "2023-10-23 01:00:00  368213.0                    NaN                NaN\n",
            "2023-11-23 01:00:00  523068.0                    NaN                NaN\n",
            "2023-12-23 01:00:00  686476.0                    NaN                NaN\n",
            "54                        NaN          444665.270837                NaN\n",
            "55                        NaN          332712.761340                NaN\n",
            "56                        NaN          321196.301142                NaN\n",
            "57                        NaN          379655.758362                NaN\n",
            "58                        NaN          510860.145138                NaN\n",
            "59                        NaN          589719.230128                NaN\n",
            "2024-01-31 01:00:00       NaN                    NaN      468639.453767\n",
            "2024-02-29 01:00:00       NaN                    NaN      330657.750189\n",
            "2024-03-31 01:00:00       NaN                    NaN      276479.092619\n"
          ]
        }
      ]
    }
  ]
}