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All Journal Journal Innovations Computer Science
Munawir Munawir
Universitas Bali Internasional Muhammadiyah Bali
Computer Science & IT

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Perbandingan Kinerja Algoritma Machine Learning untuk Prediksi Cuaca di Wilayah Tropis Indonesia: Studi Komparatif Random Forest, SVM, LSTM, XGBoost, dan LightGBM Nofirman Nofirman; Munawir Munawir; Fegie Yoanti Wattimena
Journal Innovations Computer Science Vol. 5 No. 1 (2026): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v5i1.420

Abstract

Weather prediction in tropical Indonesia faces complex challenges due to high climate variability, persistent El Niño–Southern Oscillation (ENSO) influence, and uneven observational coverage. This study compared five machine learning algorithms — Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), XGBoost, and LightGBM — using 187,320 daily records from BMKG stations, ERA5 reanalysis, and TRMM satellite data (2000–2023). Preprocessing included MMDIF-RF imputation, Z-score normalization, and SMOTE for class imbalance correction. Models were evaluated on RMSE, MAE, R², Accuracy, Precision, Recall, F1-Score, and AUC-ROC. LSTM achieved the best performance (RMSE = 3.94 mm; R² = 0.891; F1-Score = 0.887; AUC-ROC = 0.941), reflecting its capacity to capture long-range temporal dependencies. XGBoost and LightGBM delivered competitive accuracy at 8–18 times lower training cost, while SVM recorded the lowest accuracy with the highest computational demand. Regional analysis showed station density and data completeness were more consequential than algorithm choice — LSTM RMSE ranged from 3.61 mm in West Java to 5.43 mm in East Nusa Tenggara. A tiered hybrid approach is recommended: LightGBM or XGBoost for routine forecasting and LSTM for extreme event detection, alongside expanded BMKG coverage in eastern Indonesia.  
Co-Authors Fegie Yoanti Wattimena Nofirman, Nofirman