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+# 📊 Aduan Classification Model (IndoBERT)
+
+Model ini dilatih untuk **klasifikasi teks aduan masyarakat** dalam Bahasa Indonesia menggunakan **IndoBERT (indobenchmark/indobert-base-p1)**.  
+Model dapat mengelompokkan aduan ke dalam 4 kategori:
+
+- **DARURAT** → Situasi darurat (kebakaran, kecelakaan, bencana)
+- **PRIORITAS** → Perlu penanganan cepat (jalan rusak, kebersihan, infrastruktur)
+- **UMUM** → Informasi / pertanyaan umum
+- **LAINNYA** → Aduan lain yang tidak termasuk kategori di atas
+
+---
+
+## 📂 Files
+
+- `model.safetensors` → model terlatih (498MB)
+- `aduan_model.pt` → backup format pickle
+- `config.json`, `tokenizer.json`, `vocab.txt` → konfigurasi dan tokenizer
+- `special_tokens_map.json`, `tokenizer_config.json` → mapping tokenizer
+
+---
+
+## 📊 Dataset & Training
+
+- **Total data (raw)**: 3,373  
+  - Darurat: 900  
+  - Prioritas: 875  
+  - Umum: 880  
+  - Lainnya: 718  
+- **Augmentasi** → 3,600 (balance 900 per kelas)  
+- **Split** → 80% Train (2880) | 20% Validation (720)  
+- **Base model** → `indobenchmark/indobert-base-p1`  
+- **Device training** → NVIDIA RTX 3050 Laptop GPU (CUDA)  
+
+---
+
+## 📈 Hasil Evaluasi
+
+- **Best Epoch** → 3  
+- **Validation Accuracy** → **93.89%**  
+- **Macro F1-score** → **0.9389**
+
+### 📑 Classification Report
+| Label      | Precision | Recall | F1-score |
+|------------|-----------|--------|----------|
+| Darurat    | 0.9435    | 0.9278 | 0.9356   |
+| Prioritas  | 0.9257    | 0.9000 | 0.9127   |
+| Umum       | 0.9026    | 0.9778 | 0.9387   |
+| Lainnya    | 0.9884    | 0.9500 | 0.9688   |
+| **Macro Avg** | 0.9401 | 0.9389 | 0.9389   |
+
+### 🔢 Confusion Matrix
+```
+
+[[167  10   3   0]   # Darurat
+[  6 162  11   1]   # Prioritas
+[  1   2 176   1]   # Umum
+[  3   1   5 171]]  # Lainnya
+
+````
+
+---
+
+## 🧪 Contoh Prediksi
+
+### Single Input
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "Zulkifli1409/aduan-model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "Ada kebakaran besar di jalan sudirman, tolong kirim pemadam!"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+outputs = model(**inputs)
+probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+
+pred_idx = torch.argmax(probs).item()
+labels = ["DARURAT", "PRIORITAS", "UMUM", "LAINNYA"]
+
+print("Prediksi:", labels[pred_idx])
+print("Probabilitas:", probs.tolist())
+````
+
+### Output:
+
+```
+Prediksi: DARURAT
+Probabilitas: [[0.9823, 0.0145, 0.0021, 0.0011]]
+```
+
+---
+
+## 📦 Advanced Prediction Tests
+
+| Teks Aduan                                | Prediksi  | Confidence |
+| ----------------------------------------- | --------- | ---------- |
+| ada kebakaran besar di pasar tolong cepat | DARURAT   | 60.62%     |
+| jalan berlubang perlu diperbaiki          | PRIORITAS | 78.47%     |
+| mohon pencerahan tentang program desa     | UMUM      | 72.09%     |
+| ada orang kecelakaan parah butuh ambulans | DARURAT   | 74.29%     |
+| sampah menumpuk di jalan                  | PRIORITAS | 71.17%     |
+| banjir tinggi merendam rumah warga        | DARURAT   | 58.01%     |
+
+---
+
+## 🚀 Deployment
+
+Model ini juga tersedia dalam bentuk API di Railway:
+
+```
+Base URL: https://api-klasifikasi-aduan.up.railway.app
+```
+
+Contoh request:
+
+```bash
+curl -X POST https://api-klasifikasi-aduan.up.railway.app/predict \
+  -H "Content-Type: application/json" \
+  -d '{"text": "Ada kebakaran di pasar"}'
+```
+
+Response:
+
+```json
+{
+  "label": "DARURAT",
+  "confidence": 0.9823,
+  "all_scores": {
+    "DARURAT": 0.9823,
+    "PRIORITAS": 0.0145,
+    "UMUM": 0.0021,
+    "LAINNYA": 0.0011
+  }
+}
+```
+
+---
+
+## 📧 Kontak
+
+Dikembangkan oleh **Zulkifli1409**
+Jika ada pertanyaan atau saran, silakan buka *issue* atau hubungi via [Hugging Face profile](https://huggingface.co/Zulkifli1409).
+
+---
+
+**© 2025 Klasifikasi Aduan Model**