UPDATE ALL FEATURE

.vercelignore

@@ -1,19 +0,0 @@
-# Exclude heavy python backend files from Vercel deployment
-app.py
-train.py
-requirements.txt
-Dockerfile
-*.pkl
-*.csv
-*.txt
-__pycache__/
-.system_generated/
-.gemini/
-scratch/
-.agents/
-.system/
-*.log
-postman_collection.json
-FRONTEND_API_DOC.md
-Doc.md
-PUBLIC_DOC.md

README.md

@@ -8,152 +8,118 @@ app_file: app.py
 pinned: false
 ---
 
-# 🚛 Aeterna AI: Next-Gen Waste Intelligence Platform
-**Platform Sistem Peringatan Dini & Peramalan Sampah Real-Time DKI Jakarta**
 
-Aeterna AI (sebelumnya Eco-Twin AI) adalah platform analitik cerdas berbasis *Machine Learning* yang dirancang untuk memantau, memprediksi, dan mengoptimalkan manajemen logistik sampah DKI Jakarta secara spasial-temporal harian.
 
-Sistem ini didesain menggunakan **Clean Architecture** (memisahkan frontend statis yang di-host di Vercel dan backend model AI di Hugging Face Spaces) dengan performa model prediksi yang sangat tinggi.
 
----
 
-## 🌟 Fitur Unggulan (Key Features)
 
-1. **AI Autopilot Forecaster**: AI berjalan secara asinkron dan mandiri untuk memprediksi volume timbulan sampah harian di seluruh **44 Kecamatan DKI Jakarta** secara paralel berdasarkan cuaca live tingkat koordinat dan kalender event aktif hari ini.
-2. **6-Kategori Komposisi Sampah**: Memprediksi rincian tonase sampah secara proporsional sesuai statistik riil Dinas Lingkungan Hidup (DLH) DKI Jakarta menjadi 6 kategori: *Sisa Makanan (~50.2%), Plastik (~22.8%), Kertas (~11.5%), Tekstil (~4.2%), Kaca (~3.2%), dan Logam/Lainnya (~8.1%)*.
-3. **Integrasi Cuaca Live Open-Meteo**: API menarik data curah hujan real-time per kecamatan berdasarkan titik koordinat geografis asli untuk mengukur penambahan berat sampah basah akibat resapan air hujan (2% s.d. 5% multiplier).
-4. **Kalender Event Jakarta 2026**: Mengidentifikasi jadwal acara besar Jakarta (seperti PRJ JIExpo, BTN Marathon, HUT RI di Monas, dll.) untuk menghitung lonjakan kapasitas sampah kerumunan (15% s.d. 30% multiplier).
-5. **Interactive Cyber HUD UI**: Antarmuka bertema *Dark Glassmorphism* modern yang terinspirasi oleh floodzy.id dengan kursor interaktif kustom, visualisasi progress bar kategori neon glow, rincian logistik armada truk, dan efek radar sweep live di peta.
+
+# 🌍 Eco-Twin AI: Waste Volume Prediction System
+**Proyek untuk Hackathon DKI Jakarta 2026 (Case 2)**
+
+Eco-Twin AI adalah sistem cerdas berbasis *Machine Learning* yang dirancang untuk memprediksi lonjakan volume timbulan sampah harian di area Jakarta Pusat. Sistem ini menggunakan arsitektur ganda: **Amazon Chronos-T5** (Time-Series Transformer) untuk peramalan (*forecasting*) dan integrasi Algoritma Pendukung untuk ekstraksi fitur lanjutan (Cuaca, Skala Keramaian, dan Jadwal Event).
 
 ---
 
-## 📊 Hasil Evaluasi & Akurasi Model GBR
+> [!IMPORTANT]
+> **📖 DOKUMENTASI SISTEM & INTEGRASI**:  
+> 1. **Untuk Publik / Stakeholder**: Silakan merujuk ke [PUBLIC_DOC.md](file:///c:/khusus%20project%20IT/Fine%20tuning%20ulang%20AI%20jakarta/waste-prediction-api/PUBLIC_DOC.md) untuk melihat ringkasan tingkat tinggi, pemodelan AI, cara kerja sistem, serta panduan lengkap penggunaan dashboard bagi pengguna umum.
+> 2. **Untuk Tim Front-End (FE)**: Silakan merujuk ke [FRONTEND_API_DOC.md](file:///c:/khusus%20project%20IT/Fine%20tuning%20ulang%20AI%20jakarta/waste-prediction-api/FRONTEND_API_DOC.md) untuk melihat spesifikasi detail endpoint API, tipe data TypeScript, contoh kode Axios/Fetch, serta panduan pemetaan data logistik ke UI Dashboard.
+> 3. **Pengujian API (Postman)**: Anda bisa mengimpor file [waste_intelligence_api.postman_collection.json](file:///c:/khusus%20project%20IT/Fine%20tuning%20ulang%20AI%20jakarta/waste-prediction-api/waste_intelligence_api.postman_collection.json) langsung ke aplikasi Postman Anda untuk menguji seluruh endpoint secara instan.
 
-Model inti Gradient Boosting Regressor (GBR) dilatih menggunakan **GridSearchCV** untuk mencari hyperparameter terbaik di atas dataset historis teraugmentasi 2 tahun dengan baseline rata-rata kota **8.020 Ton/hari**.
+---
+
+## 🚀 Fitur Unggulan (Hackathon Killer Features)
 
-### Metrik Evaluasi Model:
+1. **Integrasi Kalender Event Otomatis**: Sistem secara otomatis membaca file `event_jakarta_2025.txt` saat server dinyalakan. Jika ada *request* prediksi yang menyentuh tanggal konser besar (misal: Maroon 5 di JIS), AI akan mendeteksi dan secara akurat menambahkan estimasi volume sampah tanpa input manual tambahan.
+2. **Asynchronous API Processing**: Menggunakan FastAPI dengan `run_in_threadpool`, memastikan sistem AI tidak memblokir (*blocking*) pengguna lain saat sedang mengolah model Transformer yang berat.
+3. **Standar Produksi (CORS & Logging)**: Aplikasi aman dipanggil secara langsung oleh Frontend (React/Vue/HTML) dan menggunakan sistem *logging* kelas enterprise.
+4. **Interactive API Docs (Swagger UI)**: Endpoint dilengkapi parameter Pydantic lengkap beserta contoh JSON terisi otomatis, sangat cocok untuk didemokan langsung ke Juri.
+5. **Dekomposisi Sampah SIPSN KLHK 2025**: Memprediksi bukan hanya berat total (Ton), tapi juga membedahnya menjadi *Sisa Makanan* dan *Plastik*, serta memberikan rekomendasi jumlah armada truk yang dibutuhkan.
 
-| Metrik Evaluasi | Model Baseline | Model Upgraded (Aeterna AI) | Status Performa |
-| :--- | :---: | :---: | :--- |
-| **Mean Absolute Error (MAE)** | `149.13 Ton` | **`132.29 Ton`** | Semakin Baik (Turun ⬇️) |
-| **Root Mean Squared Error (RMSE)** | `188.46 Ton` | **`165.46 Ton`** | Semakin Baik (Turun ⬇️) |
-| **R-Squared ($R^2$ Score)** | `76.02%` | **`81.51%`** | Semakin Baik (Naik ⬆️) |
-| **Mean Absolute Percentage Error (MAPE)** | `1.78%` | **`1.59%`** | **Sangat Akurat (< 10%) (⬇️)** |
+---
 
-### Hyperparameter Terbaik (GBR):
-*   `n_estimators` (Pohon keputusan): **100**
-*   `learning_rate`: **0.03**
-*   `max_depth`: **3**
-*   `subsample`: **0.9**
+## 📂 Struktur File
+
+- `app.py` : Berisi *Core Engine* API menggunakan FastAPI dan Amazon Chronos.
+- `train.py` : Script *Advanced Feature Engineering* dan pelatihan model Gradient Boosting (Eco-Twin Pro) untuk simulasi dataset.
+- `event_jakarta_2025.txt` : *Database* kalender event yang otomatis dilacak oleh AI.
+- `dataset_vibe_coder_2025.csv` : Dataset historis yang dipakai oleh model.
+- `.dockerfile` : Konfigurasi untuk men-*deploy* aplikasi ini (misalnya ke Hugging Face Spaces atau server Cloud).
+- `requirements.txt` : Daftar dependensi *library* Python.
 
 ---
 
-## 📂 Struktur Berkas (Clean Architecture)
+## 🛠️ Cara Menjalankan Sistem
 
+### 1. Instalasi Kebutuhan (Library)
+Pastikan Python sudah terinstal di laptop Anda. Buka Terminal/Command Prompt di dalam folder proyek ini, lalu jalankan:
+```bash
+pip install -r requirements.txt
+pip install chronos-forecasting
 ```
-waste-prediction-api/
-├── frontend/                  (📂 Client-side statis Vercel-ready)
-│   ├── index.html             (Dashboard Visual)
-│   ├── style.css              (Estetika HUD Neon & Animasi)
-│   ├── app.js                 (Logika Interaksi & Dynamic API routing)
-│   └── vercel.json            (Konfigurasi standalone Vercel subfolder)
-│
-├── vercel.json                (Konfigurasi Vercel root level)
-├── app.py                     (Python FastAPI Backend)
-├── train.py                   (Skrip training GBR & GridSearchCV)
-├── Dockerfile                 (Hugging Face Docker deployment)
-├── requirements.txt           (Python library dependencies)
-├── model_sampah_advanced.pkl  (Binary Model GBR)
-├── latest_waste_news.json     (Database Berita Rill)
-└── event_jakarta_2026.txt     (Jadwal Event Jakarta 2026)
+
+### 2. Menjalankan Server API
+Jalankan server Uvicorn dengan mode *auto-reload* agar perubahan kode langsung terbaca:
+```bash
+uvicorn app:app --reload --port 8001
 ```
 
----
+### 3. Menguji via Swagger (Demonstrasi Juri)
+Setelah server berjalan, buka browser dan akses:
+👉 **[http://127.0.0.1:8001/docs](http://127.0.0.1:8001/docs)**
 
-## 📡 Dokumentasi Endpoint API Utama
-
-Semua endpoint didukung dengan dokumentasi interaktif Swagger UI di `/docs`.
-
-### 1. Predict Waste Volume (Forecasting)
-*   **Method**: `POST`
-*   **Endpoint**: `/api/v1/predict`
-*   **Request Body**:
-    ```json
-    {
-      "forecast_days": 7,
-      "rainfall_mm": 0.0,
-      "event_scale": 0,
-      "location": "Menteng",
-      "model_type": "gradient_boosting",
-      "granularity": "daily"
-    }
-    ```
-*   **Response JSON (Truncated)**:
-    ```json
-    {
-      "status": "success",
-      "confidence_score": 0.92,
-      "message": "Normal conditions.",
-      "data": {
-        "prediction_results": [
-          {
-            "date": "2026-07-11",
-            "total_volume_ton": 120.80,
-            "organic_waste_ton": 60.64,
-            "plastic_waste_ton": 27.54,
-            "paper_waste_ton": 13.89,
-            "metal_waste_ton": 9.78,
-            "glass_waste_ton": 3.87,
-            "textile_waste_ton": 5.07,
-            "other_waste_ton": 0.00,
-            "risk_status": "SAFE",
-            "event_info": null,
-            "recommended_trucks": 25
-          }
-        ]
-      }
-    }
-    ```
-
-### 2. AI Autopilot
-*   **Method**: `GET`
-*   **Endpoint**: `/api/v1/autopilot`
-*   **Description**: Mengembalikan kalkulasi prediksi otonom hari ini untuk seluruh 44 kecamatan DKI secara paralel.
-
-### 3. News Feed
-*   **Method**: `GET`
-*   **Endpoint**: `/api/v1/news`
-*   **Description**: Mengembalikan 12 berita riil DKI Jakarta lengkap dengan link langsung ke artikel aslinya.
+Anda bisa menekan tombol **"Try it out"** di *endpoint* `/api/v1/predict` dan langsung tekan **"Execute"**.
 
 ---
 
-## 🚀 Panduan Deployment
-
-### 1. Deploy ke Hugging Face Spaces (Backend API)
-Aplikasi ini sudah dikonfigurasi untuk berjalan di Hugging Face Spaces menggunakan Docker:
-1. Buat Space baru di Hugging Face dengan memilih **SDK: Docker**.
-2. Hubungkan Git Anda atau unggah file di root direktori (termasuk `app.py`, `Dockerfile`, `requirements.txt`, dan `model_sampah_advanced.pkl`).
-3. Port internal Docker sudah otomatis disetel ke `7860` (standar Hugging Face). Space Anda akan otomatis memuat dan menjalankan backend.
+## 📡 Dokumentasi Endpoint API
+
+### 1. Status Check
+Mengecek apakah server hidup dan berapa banyak jadwal event yang berhasil dimuat oleh AI.
+- **URL**: `/`
+- **Method**: `GET`
+- **Response**:
+```json
+{
+  "status": "Online",
+  "model": "Chronos-T5 Tiny",
+  "region": "Jakarta Pusat",
+  "events_loaded": 15
+}
+```
 
-### 2. Deploy ke Vercel (Frontend Dashboard)
-Frontend dirancang agar terpisah dan di-host di Vercel:
-1. Hubungkan repositori GitHub Anda ke akun Vercel.
-2. Buat proyek baru dan pilih repositori `Aeterna-Ai`.
-3. Di bagian pengaturan Vercel, Anda dapat membiarkannya default (Vercel akan mendeteksi `vercel.json` di root) atau mengatur **Root Directory** langsung ke folder `frontend/`.
-4. Vercel akan menyajikan frontend statis dan secara otomatis mem-proxy pemanggilan API `/api` langsung ke backend Hugging Face Space Anda tanpa kendala CORS.
+### 2. Prediksi Volume Sampah (Forecasting)
+Mendapatkan peramalan volume sampah berdasarkan data historis, cuaca, dan event.
+- **URL**: `/api/v1/predict`
+- **Method**: `POST`
+- **Body Request**:
+```json
+{
+  "hari_ke_depan": 7,
+  "prediksi_hujan_bmkg": 25.5,
+  "skala_keramaian": 0
+}
+```
+- **Response JSON**:
+```json
+[
+  {
+    "tanggal": "2026-02-01",
+    "total_volume_ton": 1520.45,
+    "sisa_makanan_ton": 758.25,
+    "plastik_ton": 348.94,
+    "rekomendasi_truk": 153,
+    "status_risiko": "CRITICAL ⚠️",
+    "info_event": "Konser Maroon 5 di Jakarta International Stadium (JIS)"
+  }
+]
+```
 
 ---
 
-## 🛠️ Pengembangan Lokal (Local Development)
+## 📝 Catatan Penting
+- Jika Anda mendapatkan error `ModuleNotFoundError: No module named 'chronos'`, pastikan Anda menginstal package dengan perintah `pip install chronos-forecasting` **(BUKAN pip install chronos)**.
+- Untuk deployment dengan `Dockerfile`, pastikan untuk mengubah port Uvicorn menyesuaikan provider (misal: HuggingFace Spaces menggunakan `--port 7860`).
 
-### 1. Instalasi Dependensi
-```bash
-pip install -r requirements.txt
-```
 
-### 2. Jalankan Server API Lokal
-```bash
-python -m uvicorn app:app --port 8001 --host 127.0.0.1
-```
-Akses UI lokal di: **[http://localhost:8001](http://localhost:8001)**  
-Akses Swagger Docs di: **[http://localhost:8001/docs](http://localhost:8001/docs)**

app.py

@@ -14,12 +14,9 @@ import io
 import csv
 import json
 from chronos import ChronosPipeline
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timedelta
 import os, logging, re
 
-def get_jakarta_now() -> datetime:
-    return datetime.now(timezone(timedelta(hours=7)))
-
 # ==========================================
 # 1. APPLICATION CONFIGURATION
 # ==========================================
@@ -41,9 +38,9 @@ app.add_middleware(
 )
 
 # Mount static files to serve the dashboard UI, CSS, and JS
-if not os.path.exists("frontend"):
-    os.makedirs("frontend")
-app.mount("/static", StaticFiles(directory="frontend"), name="static")
+if not os.path.exists("static"):
+    os.makedirs("static")
+app.mount("/static", StaticFiles(directory="static"), name="static")
 
 # ==========================================
 # 2. 44 KECAMATAN DATABASE (DLH Jakarta Calibrated)
@@ -304,10 +301,10 @@ async def load_assets():
 def serve_dashboard():
     """Serve the Floodzy-style interactive dashboard."""
     try:
-        with open("frontend/index.html", "r", encoding="utf-8") as f:
+        with open("static/index.html", "r", encoding="utf-8") as f:
             return HTMLResponse(content=f.read(), status_code=200)
     except FileNotFoundError:
-        return HTMLResponse(content="<h1>Dashboard HTML not found. Please check your frontend directory.</h1>", status_code=404)
+        return HTMLResponse(content="<h1>Dashboard HTML not found. Please check your static directory.</h1>", status_code=404)
 
 @app.get("/status", tags=["System"])
 def status_check():
@@ -334,7 +331,7 @@ def get_latest_news():
         "title": "DKI Uji Coba Penarikan Retribusi Sampah Pelayanan Kebersihan Harian",
         "source": "Antara News",
         "url": "https://www.antaranews.com/tag/sampah-jakarta",
-        "date_fetched": str(get_jakarta_now().date()),
+        "date_fetched": str(datetime.now().date()),
         "summary": "Pemprov DKI Jakarta merencanakan uji coba penarikan retribusi pelayanan kebersihan/sampah."
       }
     ]
@@ -349,7 +346,7 @@ async def predict_waste_volume(req: PredictionRequest):
         raise HTTPException(503, "Models not ready.")
     
     try:
-        start_date = parse_flexible_date(req.start_date) if req.start_date else pd.Timestamp(get_jakarta_now().date())
+        start_date = parse_flexible_date(req.start_date) if req.start_date else pd.Timestamp(datetime.now().date())
         
         # Get location metadata
         config = KECAMATAN_DATABASE[req.location]
@@ -532,7 +529,7 @@ async def get_alerts(location: str = Query(None)):
     if df_history is None: raise HTTPException(503, "Model not ready")
     
     alerts = []
-    today = get_jakarta_now().date()
+    today = datetime.now().date()
     
     for i in range(3):
         d = (today + timedelta(days=i)).strftime("%Y-%m-%d")
@@ -554,7 +551,7 @@ async def get_alerts(location: str = Query(None)):
                     "message": f"Alert: {status} volume expected at {loc}"
                 })
                 
-    return AlertResponse(status="success", alert_count=len(alerts), alerts=alerts, last_updated=get_jakarta_now().isoformat())
+    return AlertResponse(status="success", alert_count=len(alerts), alerts=alerts, last_updated=datetime.now().isoformat())
 
 @app.get("/api/v1/autopilot", tags=["Autonomous"])
 async def get_autopilot_data():
@@ -562,7 +559,7 @@ async def get_autopilot_data():
     if df_history is None:
         raise HTTPException(503, "Models not ready")
         
-    today = get_jakarta_now()
+    today = datetime.now()
     d_str = today.strftime("%Y-%m-%d")
     
     total_vol = 0.0

dataset_advanced_eco_twin.csv

@@ -1,732 +1,732 @@
 Tanggal,Ada_Event,Penumpang_MRT,Curah_Hujan_mm,Hari_Dalam_Minggu,Bulan,Is_Weekend,Hujan_Kemarin,Volume_Sampah_Ton
-2023-01-01,1.0,101462,6.552432396204131,6,1,1,0.0,10219.56
-2023-01-02,0.0,74752,8.221208931545307,0,1,0,6.552432396204131,8231.66
-2023-01-03,0.0,76701,0.0,1,1,0,8.221208931545307,8033.93
-2023-01-04,0.0,71710,0.0,2,1,0,0.0,7985.06
-2023-01-05,0.0,80008,4.8408082404220805,3,1,0,0.0,8028.58
-2023-01-06,0.0,73624,18.241179328611423,4,1,0,4.8408082404220805,8074.98
-2023-01-07,0.0,90969,0.0,5,1,1,18.241179328611423,8791.29
-2023-01-08,0.0,87177,12.828050876871595,6,1,1,0.0,8652.96
-2023-01-09,0.0,97374,3.0744221347400336,0,1,0,12.828050876871595,7988.85
-2023-01-10,0.0,101080,13.409416368242386,1,1,0,3.0744221347400336,8191.25
-2023-01-11,0.0,77118,5.991440391668232,2,1,0,13.409416368242386,7990.61
-2023-01-12,0.0,80043,21.95779239956758,3,1,0,5.991440391668232,8341.42
-2023-01-13,0.0,91471,0.0,4,1,0,21.95779239956758,8367.02
-2023-01-14,0.0,89453,5.27734249049437,5,1,1,0.0,8745.8
-2023-01-15,0.0,91488,3.301896930516799,6,1,1,5.27734249049437,8660.51
-2023-01-16,0.0,112155,4.123884066270234,0,1,0,3.301896930516799,7958.8
-2023-01-17,0.0,90075,5.374918745709645,1,1,0,4.123884066270234,8115.21
-2023-01-18,0.0,96547,5.181516894053085,2,1,0,5.374918745709645,8248.08
-2023-01-19,0.0,57146,17.895461828810447,3,1,0,5.181516894053085,8040.67
-2023-01-20,0.0,85918,3.1538825233216854,4,1,0,17.895461828810447,7799.92
-2023-01-21,0.0,98052,2.7983245967656005,5,1,1,3.1538825233216854,8839.17
-2023-01-22,0.0,69480,10.742544442054077,6,1,1,2.7983245967656005,8727.51
-2023-01-23,0.0,84846,10.183602625818612,0,1,0,10.742544442054077,8290.79
-2023-01-24,0.0,86988,34.14004137990503,1,1,0,10.183602625818612,8252.33
-2023-01-25,0.0,84123,25.34362175653476,2,1,0,34.14004137990503,8319.81
-2023-01-26,0.0,89269,0.0,3,1,0,25.34362175653476,8500.1
-2023-01-27,0.0,92078,13.9787327194916,4,1,0,0.0,7912.92
-2023-01-28,0.0,75787,29.22444723286304,5,1,1,13.9787327194916,9080.92
-2023-01-29,0.0,89124,0.0,6,1,1,29.22444723286304,8716.45
-2023-01-30,0.0,89569,12.397438200922087,0,1,0,0.0,8109.43
-2023-01-31,0.0,84675,0.0,1,1,0,12.397438200922087,8044.67
-2023-02-01,0.0,100949,6.740581582972016,2,2,0,0.0,7923.99
-2023-02-02,0.0,104830,11.786975998125158,3,2,0,6.740581582972016,8280.86
-2023-02-03,0.0,78207,17.03035155606304,4,2,0,11.786975998125158,8127.42
-2023-02-04,0.0,75840,0.0,5,2,1,17.03035155606304,8858.31
-2023-02-05,0.0,83460,11.46879556319734,6,2,1,0.0,8665.59
-2023-02-06,0.0,84105,9.508395107355517,0,2,0,11.46879556319734,7957.22
-2023-02-07,0.0,70842,8.718218547674383,1,2,0,9.508395107355517,8191.68
-2023-02-08,0.0,80783,5.485269744370035,2,2,0,8.718218547674383,8217.74
-2023-02-09,0.0,64950,14.686644830108957,3,2,0,5.485269744370035,8007.12
-2023-02-10,0.0,88599,5.424155678069144,4,2,0,14.686644830108957,8145.23
-2023-02-11,0.0,83523,2.520235312944895,5,2,1,5.424155678069144,8858.68
-2023-02-12,0.0,76525,22.69045131239448,6,2,1,2.520235312944895,8576.38
-2023-02-13,0.0,73057,20.128426994459893,0,2,0,22.69045131239448,8238.41
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frontend/vercel.json

@@ -1,6 +0,0 @@
-{
-  "cleanUrls": true,
-  "rewrites": [
-    { "source": "/api/(.*)", "destination": "https://alamdieng-waste-prediction-api.hf.space/api/$1" }
-  ]
-}

latest_waste_news.json

@@ -2,84 +2,84 @@
   {
     "title": "DKI Uji Coba Penarikan Retribusi Sampah Pelayanan Kebersihan Harian",
     "source": "Antara News",
-    "url": "https://www.antaranews.com/berita/4405206/dki-uji-coba-penarikan-retribusi-sampah-desember-ini",
+    "url": "https://www.antaranews.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Pemprov DKI Jakarta merencanakan uji coba penarikan retribusi pelayanan kebersihan/sampah berdasarkan golongan daya listrik rumah tangga. Info terbaru dapat dipantau di kanal topik khusus Antara."
   },
   {
     "title": "Darurat Sampah Jakarta: Evaluasi Pengolahan Sampah Hulu dan Hilir",
     "source": "Kompas.com",
-    "url": "https://megapolitan.kompas.com/read/2023/10/18/18275101/evaluasi-sampah-jakarta-kapasitas-tpa-bantargebang",
+    "url": "https://www.kompas.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Analisis timbulan sampah tahunan DKI Jakarta dan kebijakan pemilahan sampah mandiri dari tingkat RT/RW dan pengelola kawasan komersial terpantau di Kompas."
   },
   {
     "title": "Dinas Lingkungan Hidup DKI Jakarta Antisipasi Penumpukan Sampah",
     "source": "Detik.com",
-    "url": "https://news.detik.com/berita/d-7434190/dlh-dki-siapkan-armada-antisipasi-timbulan-sampah-acara-kenegaraan",
+    "url": "https://www.detik.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Langkah mitigasi penumpukan sampah di tempat penampungan sementara (TPS) pasar tradisional dan pengerahan armada truk pengangkut sampah dapat dilihat di Detik."
   },
   {
     "title": "Kondisi TPST Bantargebang Terkini: Kapasitas Tampung Maksimal",
     "source": "Antara News",
-    "url": "https://www.antaranews.com/berita/4202781/dlh-dki-sebut-tpst-bantargebang-sudah-melebihi-kapasitas",
+    "url": "https://www.antaranews.com/tag/tpst-bantargebang",
     "date_fetched": "2026-07-10",
     "summary": "Perkembangan kapasitas TPST Bantargebang dan regulasi pembatasan sampah residu dari Jakarta menuju TPA Bekasi dipantau secara langsung di topik Antara."
   },
   {
     "title": "Pembangunan Fasilitas RDF (Refuse Derived Fuel) Terbesar di Bantargebang Selesai",
     "source": "Antara News",
-    "url": "https://www.antaranews.com/berita/3607233/dki-operasikan-fasilitas-pengolahan-sampah-terbesar-di-bantargebang",
+    "url": "https://www.antaranews.com/tag/tpst-bantargebang",
     "date_fetched": "2026-07-10",
     "summary": "Fasilitas RDF baru di TPST Bantargebang mampu mengolah ribuan ton sampah menjadi bahan bakar alternatif setara batu bara untuk pabrik semen. Ini merupakan pencapaian strategis DLH."
   },
   {
     "title": "DPRD DKI Minta Pembangunan ITF Sunter Tetap Dilanjutkan untuk Atasi Sampah",
     "source": "Kompas.com",
-    "url": "https://megapolitan.kompas.com/read/2023/11/02/10005701/dprd-dki-minta-pembangunan-itf-sunter-tetap-dilanjutkan-untuk-atasi",
+    "url": "https://www.kompas.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Dewan Perwakilan Rakyat Daerah (DPRD) DKI Jakarta meminta agar proyek pembangunan Intermediate Treatment Facility (ITF) Sunter tetap menjadi prioritas utama demi mengurangi beban harian Bantargebang."
   },
   {
     "title": "Penerapan Perda Larangan Kantong Plastik Sekali Pakai di Pasar Rakyat Diperketat",
     "source": "Detik.com",
-    "url": "https://news.detik.com/berita/d-5075191/perda-larangan-kantong-plastik-sekali-pakai-dki-mulai-berlaku",
+    "url": "https://www.detik.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Petugas Satpol PP dan DLH DKI melakukan inspeksi mendadak di beberapa pasar tradisional untuk memastikan pedagang dan pembeli beralih ke kantong belanja ramah lingkungan."
   },
   {
     "title": "TPS 3R Pejaten Barat Sukses Kurangi Sampah Hingga 15 Ton per Hari",
     "source": "Kompas.com",
-    "url": "https://megapolitan.kompas.com/read/2024/02/15/15002701/tps-3r-pejaten-barat-sukses-kurangi-sampah-15-ton",
+    "url": "https://www.kompas.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Fasilitas Tempat Pengolahan Sampah 3R di Pejaten Barat Jakarta Selatan mencatatkan keberhasilan besar dalam mereduksi volume sampah organik melalui program pengomposan mandiri."
   },
   {
     "title": "Wacana Pembuatan Pulau Sampah di Kepulauan Seribu Menimbulkan Pro-Kontra",
     "source": "Kompas.com",
-    "url": "https://megapolitan.kompas.com/read/2024/05/27/12345601/wacana-pulau-sampah-kepulauan-seribu-pro-kontra",
+    "url": "https://www.kompas.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Pemerintah Provinsi DKI menggulirkan rencana reklamasi pulau berbasis material sampah non-organik terkompresi di kawasan laut utara. WALHI meminta studi amdal diperketat."
   },
   {
     "title": "Gerakan Sedekah Sampah Berbasis Masjid Mulai Dikembangkan di Jakarta Pusat",
     "source": "Detik.com",
-    "url": "https://news.detik.com/berita/d-6789120/gerakan-sedekah-sampah-masjid-jakarta-pusat",
+    "url": "https://www.detik.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Dewan Masjid Indonesia (DMI) DKI Jakarta meluncurkan wadah sedekah sampah di mana jamaah mengumpulkan botol plastik bekas untuk didaur ulang guna mendukung kas operasional sosial masjid."
   },
   {
     "title": "Aplikasi Bank Sampah Digital Diadopsi Luas Warga Jakarta Barat",
     "source": "Antara News",
-    "url": "https://jakarta.antaranews.com/berita/4123456/warga-jakbar-manfaatkan-aplikasi-bank-sampah-digital",
+    "url": "https://www.antaranews.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Warga kini dapat menyetor sampah rumah tangga yang sudah dipilah langsung lewat aplikasi seluler dan mencairkan hasilnya ke saldo e-wallet secara instan."
   },
   {
     "title": "Komunitas Eco-Enzyme Jakarta Selatan Olah Sampah Buah Menjadi Cairan Pembersih",
     "source": "Kompas.com",
-    "url": "https://megapolitan.kompas.com/read/2024/06/18/14352001/komunitas-eco-enzyme-jaksel-olah-limbah-buah",
+    "url": "https://www.kompas.com/tag/sampah-jakarta",
     "date_fetched": "2026-07-10",
     "summary": "Ibu-ibu Pemberdayaan Kesejahteraan Keluarga (PKK) di Jakarta Selatan secara rutin mengumpulkan limbah kulit buah dari pedagang pasar untuk dirubah menjadi eco-enzyme multiguna."
   }

model_sampah_advanced.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9ef81504b813ec0801d37d86e68195c06dcad0562829c78599099e7c3cb94a8a
-size 145129
+oid sha256:5711ef9b09434b47c83f128a5d048bf1503cfd9746716355f01ebefbeef2bfb2
+size 143833

{frontend → static}/app.js

@@ -59,11 +59,6 @@ const KECAMATAN_DATABASE = {
 
 const BANTARGEBANG_COORDS = [-6.3477, 106.9939];
 
-// Dynamic backend routing (highly compatible with Vercel deployment)
-const API_BASE_URL = window.location.hostname === "localhost" || window.location.hostname === "127.0.0.1"
-    ? "" // Relative path on local environment
-    : "https://alamdieng-waste-prediction-api.hf.space"; // Direct backend url on remote hosting
-
 // UI Elements
 const locationSelect = document.getElementById("location-select");
 const modelSelect = document.getElementById("model-select");
@@ -425,7 +420,7 @@ async function runPrediction() {
     };
 
     try {
-        const response = await fetch(`${API_BASE_URL}/api/v1/predict`, {
+        const response = await fetch("/api/v1/predict", {
             method: "POST",
             headers: {
                 "Content-Type": "application/json"
@@ -611,7 +606,7 @@ async function loadNewsFeed() {
     newsGrid.innerHTML = '<div class="loading-news">Loading latest waste intelligence...</div>';
     
     try {
-        const res = await fetch(`${API_BASE_URL}/api/v1/news`);
+        const res = await fetch("/api/v1/news");
         if (res.ok) {
             const news = await res.json();
             newsGrid.innerHTML = "";
@@ -647,7 +642,7 @@ async function loadAlertsFeed() {
     alertsList.innerHTML = '<div class="loading-alerts">Evaluating regional alert parameters...</div>';
     
     try {
-        const res = await fetch(`${API_BASE_URL}/api/v1/alerts`);
+        const res = await fetch("/api/v1/alerts");
         if (res.ok) {
             const alertData = await res.json();
             alertsList.innerHTML = "";
@@ -703,7 +698,7 @@ async function loadAutopilotFeed() {
     await new Promise(r => setTimeout(r, 800));
     
     try {
-        const res = await fetch(`${API_BASE_URL}/api/v1/autopilot`);
+        const res = await fetch("/api/v1/autopilot");
         if (res.ok) {
             const data = await res.json();
             
@@ -866,67 +861,3 @@ function animate() {
 }
 
 animate();
-
-// ==========================================
-// CUSTOM CYBER HUD CURSOR
-// ==========================================
-const cursorDot = document.getElementById("cursor-dot");
-const cursorRing = document.getElementById("cursor-ring");
-
-let mouseX = -100;
-let mouseY = -100;
-let ringX = -100;
-let ringY = -100;
-
-document.addEventListener("mousemove", (e) => {
-    mouseX = e.clientX;
-    mouseY = e.clientY;
-    
-    if (cursorDot && cursorDot.style.display !== "block") {
-        cursorDot.style.display = "block";
-        cursorRing.style.display = "block";
-    }
-});
-
-function animateCursor() {
-    const lerpFactor = 0.15;
-    ringX += (mouseX - ringX) * lerpFactor;
-    ringY += (mouseY - ringY) * lerpFactor;
-
-    if (cursorDot) {
-        cursorDot.style.transform = `translate3d(${mouseX}px, ${mouseY}px, 0) translate3d(-50%, -50%, 0)`;
-    }
-    if (cursorRing) {
-        cursorRing.style.transform = `translate3d(${ringX}px, ${ringY}px, 0) translate3d(-50%, -50%, 0)`;
-    }
-    requestAnimationFrame(animateCursor);
-}
-animateCursor();
-
-// Mouse hover scaling state
-document.addEventListener("mouseover", (e) => {
-    if (cursorRing && (
-        e.target.tagName === "BUTTON" || 
-        e.target.tagName === "A" || 
-        e.target.tagName === "SELECT" || 
-        e.target.tagName === "INPUT" || 
-        e.target.classList.contains("leaflet-interactive") ||
-        e.target.closest("button") || 
-        e.target.closest("a")
-    )) {
-        cursorRing.classList.add("hover-state");
-    }
-});
-document.addEventListener("mouseout", (e) => {
-    if (cursorRing && (
-        e.target.tagName === "BUTTON" || 
-        e.target.tagName === "A" || 
-        e.target.tagName === "SELECT" || 
-        e.target.tagName === "INPUT" || 
-        e.target.classList.contains("leaflet-interactive") ||
-        e.target.closest("button") || 
-        e.target.closest("a")
-    )) {
-        cursorRing.classList.remove("hover-state");
-    }
-});

{frontend → static}/index.html

@@ -413,10 +413,6 @@
         <p>&copy; 2026 Aeterna AI - DKI Jakarta Waste Management Intelligence. Calibrated with DLH & Open-Meteo.</p>
     </footer>
 
-    <!-- Custom Cyber HUD Cursor -->
-    <div id="cursor-dot"></div>
-    <div id="cursor-ring"></div>
-
     <!-- Scripts -->
     <script src="/static/app.js"></script>
 </body>

{frontend → static}/style.css

@@ -1163,144 +1163,4 @@ footer {
     color: var(--text-muted);
 }
 
-/* ==========================================
-   CUSTOM CYBER HUD CURSOR & INTERACTIVITY
-   ========================================== */
-#cursor-dot,
-#cursor-ring {
-    position: fixed;
-    top: 0;
-    left: 0;
-    pointer-events: none;
-    z-index: 10000;
-    border-radius: 50%;
-    display: none;
-    backface-visibility: hidden;
-}
-
-#cursor-dot {
-    width: 6px;
-    height: 6px;
-    background: var(--cyan);
-    box-shadow: 0 0 10px var(--cyan);
-}
-
-#cursor-ring {
-    width: 40px;
-    height: 40px;
-    border: 1px solid var(--border-hover);
-    transition: width 0.25s cubic-bezier(0.25, 1, 0.5, 1), 
-                height 0.25s cubic-bezier(0.25, 1, 0.5, 1), 
-                border-color 0.25s ease, 
-                background 0.25s ease;
-}
-
-/* Hover state on buttons/interactive elements */
-#cursor-ring.hover-state {
-    width: 55px;
-    height: 55px;
-    border-color: var(--cyan);
-    background: rgba(0, 240, 255, 0.04);
-    box-shadow: 0 0 15px rgba(0, 240, 255, 0.15);
-}
-
-@media (pointer: coarse) {
-    #cursor-dot,
-    #cursor-ring {
-        display: none !important;
-    }
-    * {
-        cursor: auto !important;
-    }
-}
-
-/* Hide default cursor on desktops for the custom cursor feel */
-@media (pointer: fine) {
-    body, a, button, select, input, [role="button"], .leaflet-interactive {
-        cursor: none !important;
-    }
-}
-
-/* ==========================================
-   RADAR SWEEP EFFECT (ON MAP OVERLAY)
-   ========================================== */
-.map-container {
-    position: relative;
-}
-
-.map-container::after {
-    content: '';
-    position: absolute;
-    inset: 0;
-    pointer-events: none;
-    z-index: 1000;
-    background: conic-gradient(from 0deg at 50% 50%, rgba(0, 240, 255, 0.08) 0deg, transparent 90deg, transparent 360deg);
-    animation: radar-sweep 8s linear infinite;
-    opacity: 0.7;
-    border-radius: 8px;
-    mix-blend-mode: screen;
-}
-
-@keyframes radar-sweep {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-
-/* ==========================================
-   AMBIENT GLOWS & GLASSMORPHISM UPGRADES
-   ========================================== */
-body::before {
-    content: '';
-    position: fixed;
-    top: -10%;
-    left: -10%;
-    width: 50%;
-    height: 50%;
-    background: radial-gradient(circle, rgba(0, 240, 255, 0.05) 0%, transparent 70%);
-    z-index: 0;
-    pointer-events: none;
-}
-
-body::after {
-    content: '';
-    position: fixed;
-    bottom: -10%;
-    right: -10%;
-    width: 60%;
-    height: 60%;
-    background: radial-gradient(circle, rgba(0, 102, 255, 0.04) 0%, transparent 70%);
-    z-index: 0;
-    pointer-events: none;
-}
-
-/* Neon glow for progress bars */
-.progress-bar-fill.organic {
-    box-shadow: 0 0 8px var(--green-glow);
-}
-.progress-bar-fill.plastic {
-    box-shadow: 0 0 8px var(--cyan-glow);
-}
-.progress-bar-fill.paper {
-    box-shadow: 0 0 8px var(--yellow-glow);
-}
-.progress-bar-fill.glass {
-    box-shadow: 0 0 8px rgba(0, 240, 255, 0.25);
-}
-.progress-bar-fill.textile {
-    box-shadow: 0 0 8px rgba(255, 0, 85, 0.25);
-}
-.progress-bar-fill.metal {
-    box-shadow: 0 0 8px rgba(255, 255, 255, 0.25);
-}
-
-/* Logo Reflective Polish */
-.logo-text {
-    position: relative;
-    -webkit-box-reflect: below -4px linear-gradient(transparent, rgba(255, 255, 255, 0.08));
-}
-
 

train.py

@@ -17,9 +17,11 @@ if sys.platform == 'win32':
 print("🚀 MEMULAI PROSES TRAINING AI LEVEL ADVANCED (ECO-TWIN PRO)...\n")
 
 # ==========================================
-# 1. DATA INGESTION & AUGMENTATION (2 TAHUN)
+# 1. LOAD LOCALIZED DATA
 # ==========================================
-print("📥 1. Menarik & Memproses Data Historis (2023 - 2024)...")
+print("1. Menarik & Memproses Data Historis Lokal...")
+df = pd.read_csv('dataset_local_2026.csv')
+df['Tanggal'] = pd.to_datetime(df['Tanggal'])
 
 # Baseline Sampah (Diambil dari SIPSN DKI 2025)
 base_sampah = 8020.0 
@@ -52,45 +54,36 @@ df['Curah_Hujan_mm'] = np.random.exponential(scale=hujan_mean, size=len(df))
 df.loc[df['Curah_Hujan_mm'] < 2, 'Curah_Hujan_mm'] = 0
 
 # ==========================================
-# 2. ADVANCED FEATURE ENGINEERING (MIND-BLOWING)
+# 2. FEATURE ENGINEERING (LOCAL BINDING)
 # ==========================================
-print("🧠 2. Melakukan Feature Engineering (Ekstraksi Pola Waktu)...")
+print("2. Melakukan One-Hot Encoding Lokasi & Verifikasi Fitur...")
 
-# Ekstraksi Siklus Waktu
-df['Hari_Dalam_Minggu'] = df['Tanggal'].dt.dayofweek # 0=Senin, 6=Minggu
-df['Bulan'] = df['Tanggal'].dt.month
-df['Is_Weekend'] = df['Hari_Dalam_Minggu'].apply(lambda x: 1 if x >= 5 else 0)
+# Defensive manual one-hot encoding to guarantee column names and order
+locations = ['JIS', 'GBK', 'Pasar Senen', 'Gang Sempit Tambora']
+for loc in locations:
+    df[f'Loc_{loc}'] = (df['Location'] == loc).astype(int)
 
-# Lag Features (Mengingat masa lalu)
-df['Hujan_Kemarin'] = df['Curah_Hujan_mm'].shift(1).fillna(0)
+# Fitur yang dipakai AI buat berpikir
+fitur = [
+    'Loc_JIS', 'Loc_GBK', 'Loc_Pasar Senen', 'Loc_Gang Sempit Tambora',
+    'RR', 'Rain_Lag_1', 'Rain_Lag_2', 'Is_Holiday', 'Ada_Event', 'Crowd_Scale',
+    'Hari_Ke', 'Is_Weekend', 'Hari_Dalam_Minggu', 'Bulan'
+]
 
-# Target Variable Generation
-df['Volume_Sampah_Ton'] = base_sampah + \
-    (df['Ada_Event'] * base_sampah * np.random.uniform(0.15, 0.30, size=len(df))) + \
-    (df['Is_Weekend'] * base_sampah * 0.08) + \
-    (df['Curah_Hujan_mm'] / 50 * base_sampah * 0.03) + \
-    (df['Hujan_Kemarin'] / 50 * base_sampah * 0.05) + \
-    ((df['Penumpang_MRT'] - mrt_harian_avg) / mrt_harian_avg * base_sampah * 0.02)
-
-# Noise (Fluktuasi harian)
-df['Volume_Sampah_Ton'] += np.random.normal(0, base_sampah*0.02, size=len(df))
-df['Volume_Sampah_Ton'] = df['Volume_Sampah_Ton'].round(2)
-
-# Simpan dataset
-df.to_csv('dataset_advanced_eco_twin.csv', index=False)
+X = df[fitur]
+y = df['Volume_Ton']
 
 # ==========================================
 # 3. CHRONOLOGICAL SPLIT & TRAINING
 # ==========================================
-print("⚙️ 3. Melatih Model AI dengan Algoritma Gradient Boosting...")
+print("3. Membagi Data secara Kronologis (75/25) & Melatih Model...")
 
-# Fitur yang dipakai AI buat mikir
-fitur = ['Penumpang_MRT', 'Ada_Event', 'Curah_Hujan_mm', 'Hujan_Kemarin', 'Hari_Dalam_Minggu', 'Bulan', 'Is_Weekend']
-X = df[fitur]
-y = df['Volume_Sampah_Ton']
+# 75% days for training, 25% for test. 
+# Since we have 4 locations per day, we split at index: (len(df) // 4 * 0.75) * 4
+num_days = len(df) // 4
+train_days = int(num_days * 0.75)
+train_size = train_days * 4
 
-# Memisahkan masa lalu (2023) buat belajar, masa depan (2024) buat ujian
-train_size = int(len(df) * 0.75) # 75% data awal
 X_train, X_test = X.iloc[:train_size], X.iloc[train_size:]
 y_train, y_test = y.iloc[:train_size], y.iloc[train_size:]
 
@@ -169,3 +162,4 @@ for name, importance in zip(fitur, importances):
 # Simpan Model Terbaik
 joblib.dump(best_model, 'model_sampah_advanced.pkl')
 print("\n💾 SUCCESS! 'model_sampah_advanced.pkl' berhasil di-generate menggunakan model hasil upgrade!")
+

vercel.json

@@ -1,9 +0,0 @@
-{
-  "version": 2,
-  "cleanUrls": true,
-  "rewrites": [
-    { "source": "/static/(.*)", "destination": "/frontend/$1" },
-    { "source": "/api/(.*)", "destination": "https://alamdieng-waste-prediction-api.hf.space/api/$1" },
-    { "source": "/(.*)", "destination": "/frontend/$1" }
-  ]
-}