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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Usman Sattar
Politeknik Bombana
Computer Science & IT Engineering

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Explainable rice yield from Sentinel-1 and Sentinel-2 satellite data for food security Dhimas Tribuana; Usman Sattar; Baharuddin Mide; Dayanti Dayanti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp615-627

Abstract

Reliable, explainable crop-yield estimates are essential for food-security planning in data-sparse regions. We present a transparent pipeline for district-level (regency) rice yield prediction in Indonesia that fuses Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 normalized difference vegetation index (NDVI), and weather/reanalysis features. The system standardizes inputs per province, fixes a 16-day temporal key, and uses a small, auditable ensemble of tree models (gradient boosting+light gradient-boosting machine (LightGBM)). Trained on ≤2023 data and evaluated on a 2024 temporal hold-out, a joint West Java ∪ South Sulawesi model achieves root mean square error (RMSE)≈0.80 t/ha, mean absolute error (MAE)≈0.48 t/ha, and R-squared (R²)≈0.33 at regency scale. Feature importances and Shapley additive explanations (SHAP) confirm that phenology (NDVI peak, integral, green-up/senescence), SAR backscatter (vertical transmit-vertical receive/vertical transmit-horizontal receive (VV/VH)), and wind/pressure are consistent drivers under monsoon conditions. The workflow supports routine, one-click provincial updates and produces parity maps and error bars for actionable diagnostics. These results demonstrate that combining Sentinel-1, Sentinel-2, and basic meteorology delivers accurate, interpretable, and operational yield signals suited to Indonesia’s food security needs, while providing a clear recipe for scaling to additional provinces.
Probabilistic Machine Learning Early Warning for Urban PM2.5 in SEA Cities Baharuddin Mide; Dhimas Tribuana; Usman Sattar; Dayanti Dayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i7199

Abstract

Air pollution, particularly fine particulate matter (PM₂.₅), poses a critical threat to public health in rapidly urbanizing regions. Reliable early-warning systems are essential for mitigating exposure risks, yet challenges remain in cities with heterogeneous sensor coverage and event frequency. This study aimed to develop and evaluate a probabilistic, portable across cities early-warning framework for PM₂.₅ exceedances in Southeast Asia, focusing on Jakarta, Singapore, and Bangkok. Using a staged experimental design (Exp-A through Exp-E), we integrated regression-based back-casts with classification-based exceedance alerts, applied variant selection across thresholds and horizons, and validated for operational readiness through model freezing, documentation, and online simulation. Results showed that Jakarta achieved near-perfect exceedance prediction up to 6-hour horizons (F1 ≈ 0.99), Singapore maintained strong performance at short horizons (F1 ≈ 0.91 at 2–3 hours), while Bangkok yielded moderate but actionable signals at very short horizons (F1 ≈ 0.62 at 1 hour). Regression components provided stable situational awareness, and online smoothing reduced false alarms by approximately 15–20% without degrading performance. The framework demonstrated that calibrated exceedance probabilities can serve as an effective basis for city-level air quality alerts, with reliability strongly influenced by data density and event prevalence. This work contributes a reproducible, transparent, and computationally efficient approach that bridges machine learning innovation with practical environmental management. The findings emphasize the importance of horizon-specific calibration and adaptive strategies, offering both theoretical insights and practical value for policymakers in urban air quality governance.
Co-Authors Baharuddin Mide Dayanti Dayanti Dhimas Tribuana Dhimas Tribuana