{"id":23694315,"url":"https://github.com/arfazrll/data-mining-competition","last_synced_at":"2025-07-24T17:03:38.315Z","repository":{"id":269142794,"uuid":"906551095","full_name":"Arfazrll/Data-Mining-Competition","owner":"Arfazrll","description":"Repository ini berisi partisipasi saya dalam kompetisi ADIKARA 2024 - Data Mining Competition. Repository ini terkait mengembangkan model prediksi Food Price Index menggunakan dataset spatiotemporal. 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Dengan dataset berbasis **indeks harga pangan**, lalu mengembangkan model yang mendukung pengambilan keputusan terkait data tersebut.\n\n---\n\n## 🎯 **Tujuan**\n- **Mengaplikasikan Data dan Machine Learning**\n- **Meningkatkan teknis dalam data mining**\n- **Mendukung pengambilan keputusan berbasis data**\n\n---\n\n## 📂 **Struktur Repository**\n\n```plaintext\nadikara2024-datamining/\n├── Notebook File/\n│   ├── Notebook1_Manusia_Pelupa_ADIKARA2024.ipynb\n│   └── Notebook2_Manusia_Pelupa_ADIKARA2024.ipynb\n├── Submission File/\n│   ├── submission_Manusia Pelupa_ADIKARA2024.csv\n│   └── test_adikara2024_unlabeled.csv\n├── adikara2024-datamining/\n│   ├── train_adikara2024.csv\n│   └── sample_submission_adikara2024.csv\n├── LICENSE\n├── README.md\n└── ...\n```\n\n---\n\n## 📊 **Dataset**\n\n| **File**                   | **Deskripsi**                                                |\n|----------------------------|------------------------------------------------------------|\n| `train_adikara2024.csv`    | Data pelatihan dengan label *Food Price Index*              |\n| `test_adikara2024_unlabeled.csv` | Data uji tanpa label, digunakan untuk prediksi                 |\n| `sample_submission_adikara2024.csv` | Contoh format file *submission* untuk leaderboard            |\n\n\u003e ⚠️ **Catatan:** Pastikan untuk Menggunakan data dengan benar.\n\n---\n\n## 📏 **Metrik Evaluasi**\nMenggunakan **Symmetric Mean Absolute Percentage Error (sMAPE)**:\n\n![sMAPE Formula](https://github.com/Arfazrll/AllReference/blob/main/sMape.png)\n\nSemakin **kecil nilai sMAPE**, semakin baik prediksi modelnya.\n\n---\n\n## 🚀 **Alur Penyelesaian**\n\n1. 📥 **Eksplorasi Data**\n2. 🛠️ **Pre-processing \u0026 Feature Engineering**\n3. 🧠 **Pemodelan**\n4. 📊 **Evaluasi Model dengan sMAPE**\n5. 💾 **Eksport Model Terbaik**\n6. 🔍 **Prediksi Data Uji**\n7. 📝 **Generate Submission File**\n\n---\n\n## 📘 **Notebook 1 - Pelatihan Model**\n**Nama File:** `Notebook1_Manusia_Pelupa_ADIKARA2024.ipynb`\n\nNotebook ini mencakup:\n- Eksplorasi data (`train_adikara2024.csv`)\n- *Pre-processing* (menangani nilai hilang, *encoding*, dsb.)\n- Pelatihan model dengan algoritma seperti Random Forest, XGBoost, dll.\n- Evaluasi model menggunakan sMAPE\n- Eksport model terbaik \n\n---\n\n## 📗 **Notebook 2 - Prediksi Submission**\n**Nama File:** `Notebook2_Manusia_Pelupa_ADIKARA2024.ipynb`\n\nNotebook ini mencakup:\n- Membaca file `test_adikara2024_unlabeled.csv`\n- Mengimpor model terbaik dari Notebook 1\n- *Pre-processing* data uji\n- Memprediksi *Food Price Index*\n- Menghasilkan file submission (`submission_Manusia Pelupa_ADIKARA2024.csv`)\n\n---\n\n## 📑 **Format Submission**\n\nBerikut format yang harus digunakan untuk file submission:\n\n```csv\nid,FoodPriceIndex\n122,20.5\n123,21.7\n124,19.8\n```\n\n---\n\n## 💻 **Cara Menjalankan**\n\n1. Clone repository ini:\n   ```bash\n   git clone https://github.com/YourUsername/adikara2024-datamining.git\n   cd adikara2024-datamining\n   ```\n\n2. Siapkan lingkungan Python (opsional):\n   ```bash\n   python -m venv env\n   source env/bin/activate  # Untuk Linux/Mac\n   env\\Scripts\\activate   # Untuk Windows\n   pip install -r All_Requirements.txt\n   ```\n\n## 🛠️ **Prasyarat**\n\nPastikan Anda memiliki:\n- Python 3.8 atau lebih baru\n- Library utama seperti `pandas`, `numpy`, `scikit-learn`, `xgboost`, dll.\n- Jupyter Notebook untuk menjalankan `.ipynb` file\n\n---\n\n## 📂 **File Pendukung**\n\n| **File/Fungsi**          | **Deskripsi** |\n|--------------------------|---------------|\n| `requirements.txt`       | Daftar library yang diperlukan untuk menjalankan kode |\n| `sample_submission.csv`  | Template untuk format submission |\n| `train.csv`              | Dataset pelatihan dengan label |\n| `test.csv`               | Dataset uji tanpa label |\n\n---\n\n## 🌐 **Teknologi yang Digunakan**\n\n- Python 🐍\n- Jupyter Notebook 📓\n- Machine Learning (Random Forest, XGBoost, dll.) 🤖\n- Pandas \u0026 Numpy untuk analisis data 📊\n- Matplotlib \u0026 Seaborn untuk visualisasi 📈\n\n---\n\n3. Jalankan notebook dengan Jupyter:\n   ```bash\n   jupyter notebook\n   ```\n\n4. Ikuti instruksi pada `Notebook1` dan `Notebook2` untuk pelatihan serta prediksi.\n\n---\n\n## 🔮 **Kesimpulan**\n\n**ADIKARA 2024 - Data Mining Competition** memberikan saya peluang luar biasa untuk meningkatkan kemampuan analisis data spatiotemporal dan machine learning. Dengan memanfaatkan metrik evaluasi seperti sMAPE, saya dapat mengembangkan model prediksi yang akurat dan relevan🚀\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farfazrll%2Fdata-mining-competition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farfazrll%2Fdata-mining-competition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farfazrll%2Fdata-mining-competition/lists"}