diff --git a/Machine_Learning/notebook.ipynb b/Machine_Learning/notebook.ipynb
index 793bf30..d863e0a 100644
--- a/Machine_Learning/notebook.ipynb
+++ b/Machine_Learning/notebook.ipynb
@@ -170,7 +170,7 @@
     "outputId": "30228ce8-1b64-47f5-93f7-30632b2af1c5"
    },
    "outputs": [],
-   "source": "if IS_COLAB:\n    drive.mount('/content/drive')\n    archive_path = '/content/drive/MyDrive/jagung/dataset_jagung.zip'\n    destination_path = '/content/dataset_jagung.zip'\n    extract_path = '/content/dataset'\nelse:\n    # Local: dataset assumed at Machine_Learning/dataset_jagung.zip\n    base = os.getcwd()\n    archive_path = os.path.join(base, 'dataset_jagung.zip')\n    destination_path = archive_path  # no copy needed\n    extract_path = os.path.join(base, 'dataset')\n\nif os.path.exists(archive_path):\n    if not IS_COLAB:\n        print(f\"Found dataset at {archive_path}\")\n    else:\n        print(\"Moving ZIP file to /content...\")\n        shutil.copy(archive_path, destination_path)\n        print(\"File successfully moved.\")\nelse:\n    print(f\"Error: File not found at {archive_path}\")\n\nif os.path.exists(destination_path):\n    if not os.path.exists(extract_path) or len(os.listdir(extract_path)) == 0:\n        os.makedirs(extract_path, exist_ok=True)\n        print(\"Extracting dataset...\")\n        try:\n            with zipfile.ZipFile(destination_path, 'r') as zip_ref:\n                zip_ref.extractall(path=extract_path)\n            print(\"Extraction completed!\")\n        except Exception as e:\n            print(f\"Extraction failed: {e}\")\n    else:\n        print(\"Dataset is already extracted and ready for use.\")"
+   "source": "if IS_COLAB:\n    drive.mount('/content/drive')\n    archive_path = '/content/drive/MyDrive/jagung/dataset_jagung.zip'\n    destination_path = '/content/dataset_jagung.zip'\n    extract_path = '/content/dataset'\nelse:\n    import gdown\n    base = os.getcwd()\n    archive_path = os.path.join(base, 'dataset_jagung.zip')\n    destination_path = archive_path\n    extract_path = os.path.join(base, 'dataset')\n    # If dataset doesn't exist locally, download from Google Drive\n    DRIVE_FILE_ID = \"1iP1J5o-wJZXslI21TF0H9txNvFgV0tFZ\"\n    if not os.path.exists(archive_path):\n        print(\"Dataset not found locally. Downloading from Google Drive...\")\n        try:\n            gdown.download(f\"https://drive.google.com/uc?id={DRIVE_FILE_ID}\", archive_path, quiet=False)\n        except Exception as e:\n            print(f\"Download failed: {e}\")\n\nif os.path.exists(destination_path):\n    if not os.path.exists(extract_path) or len(os.listdir(extract_path)) == 0:\n        os.makedirs(extract_path, exist_ok=True)\n        print(\"Extracting dataset...\")\n        try:\n            with zipfile.ZipFile(destination_path, 'r') as zip_ref:\n                zip_ref.extractall(path=extract_path)\n            print(\"Extraction completed!\")\n        except Exception as e:\n            print(f\"Extraction failed: {e}\")\n    else:\n        print(\"Dataset is already extracted and ready for use.\")"
   },
   {
    "cell_type": "markdown",
diff --git a/Machine_Learning/requirements.txt b/Machine_Learning/requirements.txt
index 5ba1f16..a1feaaf 100644
--- a/Machine_Learning/requirements.txt
+++ b/Machine_Learning/requirements.txt
@@ -1,5 +1,6 @@
 tensorflow==2.19.0              # CPU + Colab; for local GPU, install tensorflow[and-cuda]
 tensorflowjs==4.22.0
+gdown                           # download dataset from Google Drive
 numpy
 matplotlib
 seaborn