Aeterna AI: Spatial-Temporal Waste Generation Forecasting Across 44 Kecamatans in DKI Jakarta

Name: Aeterna AI
Author: Faril Putra Pratama

Faril Putra Pratama (@FARILtau72)

NEXT-GEN WASTE FORECASTING

Meningkatkan efisiensi tata kelola sampah DKI Jakarta hingga 98.28% dengan pemodelan spasial temporal real-time.

KEUNGGULAN AETERNA AI

Akurasi Validitas Tinggi (98.28%)

Menggunakan arsitektur model Gradient Boosting Regressor (GBR) yang dioptimasi via GridSearchCV dengan metrik komparasi MAE, RMSE, dan MAPE secara berdampingan.

Integrasi Live Cuaca Open-Meteo

API dinamis asinkron yang menarik data curah hujan live tingkat kecamatan secara real-time untuk memprediksi tonase sampah basah akibat rembesan air hujan.

Pemantauan Berita Sampah 1 Jam

AI Scheduler internal merayap internet secara berkala tiap 1 jam untuk menangkap isu operasional, pembatasan Bantargebang, dan regulasi persampahan DKI Jakarta.

Kalibrasi Spasial 44 Kecamatan

Model peramalan dikalibrasi hulu-hilir menggunakan data primer Dinas Lingkungan Hidup DKI Jakarta, mencakup tonase baseline spesifik tiap kecamatan secara akuntabel.

TRANSPARANSI SUMBER DATA

1. Timbulan Sampah

Data baseline disesuaikan dengan volume total ~9.059 Ton/hari dari Dinas Lingkungan Hidup (DLH) DKI Jakarta dan SIPSN Kementerian LHK.

2. Prediksi Cuaca

Data curah hujan ditarik live menggunakan Open-Meteo API berdasarkan koordinat geografis presisi masing-masing kecamatan.

3. Kalender Event & Transit

Pola mobilitas disimulasikan dari ridership harian MRT Jakarta (~85.000) dan kalender acara Jakarta untuk mengukur lonjakan kerumunan.

4. Jarak Pengangkutan

Jarak tempuh dihitung menggunakan Haversine Formula dari koordinat kecamatan ke TPST Bantargebang dengan faktor jalan winding 1.35x.

AETERNA AI LEAD DEVELOPER

Lead Full-Stack AI Engineer

FARIL PUTRA PRATAMA

Merancang dan membangun seluruh arsitektur Aeterna AI dari ujung ke ujung. Meliputi riset data, pelatihan model cerdas GBR & GridSearchCV, pembuatan arsitektur micro-service asinkron, hingga visualisasi antarmuka HUD Glassmorphism interaktif Next.js.

PREDICTION CONFIG

Target Location

AI Forecasting Model

Forecast Horizon: 7 Days

Simulation Overrides

Manual Rain Override (mm)

Auto (Open-Meteo)

Simulasi Populasi Auto (BPS Baseline)

JAKARTA SPATIAL REALTIME MAP

TOTAL FORECAST VOLUME 0.00 Tons Period: N/A

RISK STATUS SAFE Menteng

RECOMMENDED FLEET 0 Trucks (15T) Logistics Fleet Suggestion

WASTE COMPOSITION BREAKDOWN

Organic / Sisa Makanan (~50.2%) 0.00 Ton

Plastic / Plastik (~22.8%) 0.00 Ton

Paper & Cardboard / Kertas (~11.5%) 0.00 Ton

Glass & Ceramics / Kaca (~3.2%) 0.00 Ton

Textile & Leather / Tekstil (~4.2%) 0.00 Ton

Metals & Others / Logam (~8.1%) 0.00 Ton

WEATHER & EVENT FORECAST

Fetching Live... Jakarta, Indonesia

Precipitation (Rain): 0.0 mm

BMKG Alert: None

Upcoming Event Calendar

No major events registered for today.

OPERATIONAL LOGISTICS PLAN

Recommended Fleet -- Trucks 15 ton capacity / truck

Crew Requirement -- Personnel -- drivers + -- collectors

Estimated Collection Duration -- Hours Adjusted for traffic, weather & events

Required Truck Loads ~-- Loads forecast volume ÷ 15T gross capacity

Operational Efficiency --% — Optimal Multi-factor operational index

Forecast Reliability --% Empirical model quality & data health

📖 Operational Formula & Scientific Methodology Dinas Lingkungan Hidup DKI Jakarta Standards

1. Recommended Fleet (Compactor 15T)

Effective Capacity = 15.0T × 95% Load Factor = 14.25 Ton/trip
Base Trucks = ceil(Total Waste / 14.25 Ton)
Recommended Trucks = ceil(Base Trucks × 1.05 Buffer)
*Buffer 5% menampung lonjakan insidental & rotasi armada.

2. Crew Sizing (Manpower)

Standard Crew = 1 Certified Driver + 2 Sanitarian Collectors = 3 Personel/Truk
Total Personnel = Recommended Trucks × 3
*Dihitung langsung dari kebutuhan armada aktif (bukan angka acak).

3. Collection Duration (Fleet Throughput)

Fleet Throughput = Active Trucks × 2.0 Ton/Jam
Raw Hours = Total Waste / Fleet Throughput
Adjusted Hours = (Raw Hours × Traffic × Weather × Event) / Efficiency
*Berbasis throughput armada; bukan sekadar volume ÷ 15T.

4. Efficiency & Reliability Scores

Efficiency = Fleet (35%) + Weather (20%) + Traffic (20%) + Event (15%) + Utilization (10%)
Reliability = Model Quality (50%) + Data Verification (30%) + Horizon (20%)

NEXT-GEN WASTE FORECASTING

Jakarta Waste Baseline

KEUNGGULAN AETERNA AI

Akurasi Validitas Tinggi (98.28%)

Integrasi Live Cuaca Open-Meteo

Pemantauan Berita Sampah 1 Jam

Kalibrasi Spasial 44 Kecamatan

JAKARTA DAILY ACCUMULATION TOWER

2,264 TON SAMPAH TERKUMPUL

4,982 TON SAMPAH TERKUMPUL

9,059 TON SAMPAH HARIAN

TRANSPARANSI SUMBER DATA

1. Timbulan Sampah

2. Prediksi Cuaca

3. Kalender Event & Transit

4. Jarak Pengangkutan

AETERNA AI LEAD DEVELOPER

FARIL PUTRA PRATAMA

AETERNA AI AUTOPILOT FORECASTER

LIVE CITY-WIDE FORECAST (TODAY)

Top 5 High-Risk Kecamatan Today:

AI THINKING CONSOLE

PREDICTION CONFIG

Simulation Overrides

JAKARTA SPATIAL REALTIME MAP

WASTE COMPOSITION BREAKDOWN

WEATHER & EVENT FORECAST

OPERATIONAL LOGISTICS PLAN

DAILY TIMELINE BREAKDOWN

HOURLY DISPATCH RISK HEATMAP

FARIL PUTRA PRATAMA

MONITORING BERITA PERSAMPAHAN JAKARTA

REGIONAL HAZARD & ALERTS MAP

ACTIVE OPERATIONAL ALERTS

WASTE EDUCATION HUB

ECO-SORTER SIMULATOR

Botol Plastik Bekas

DURASI DEKOMPOSISI ALAM

Sisa Makanan / Organik

Kertas & Kardus

Botol Plastik PET

Baterai & Logam

Botol Kaca