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All Journal Jurnal Gaussian BAREKENG: Jurnal Ilmu Matematika dan Terapan EPSILON Jurnal Pendidikan Matematika Jurnal Pengabdian kepada Masyarakat Nusantara VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Applied Statistics, Mathematics, and Data Science Sipakaraya : Jurnal Pengabdian Masyarakat Arunika: Jurnal Pengabdian Masyarakat
Rahayu, Putri Indi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Jurnal Gaussian

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PREDIKSI KEKERINGAN DENGAN MENGGUNAKAN RANDOM FOREST DAN XGBOOST SEBAGAI STRATEGI MITIGASI BENCANA DI KABUPATEN MAJENE Hijrah, Muh.; Rahayu, Putri Indi; Wahyudi, Wahyudi
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.98-109

Abstract

The growing incidence of drought attributable to climate change poses serious challenges to food systems and agricultural livelihoods in Majene Regency, West Sulawesi. To address this, the present study compares the predictive accuracy of Random Forest and XGBoost in classifying drought occurrences from meteorological variables, using 300 monthly observations recorded by BMKG between 2000 and 2024. Exploratory data analysis revealed that 60% of drought events are concentrated in August and the target variable is severely imbalanced (16.3% drought vs. 83.7% non-drought). At the default threshold of 0.50, Random Forest outperforms XGBoost in all metrics. However, because imbalanced data causes both models to underdetect drought at the default threshold (sensitivity = 0.267), threshold optimization was applied by lowering the decision threshold to τ = 0.15. The result of threshold optimalization was significantly improved Random Forest sensitivity from 0.267 to 0.867, detecting 13 out of 15 drought events in the test set while maintaining an AUC-ROC of 0.916. Feature importance analysis consistently identifies X3 as the dominant predictor in both models. Random Forest with threshold τ = 0.15 is recommended as the primary model for drought early warning systems in Majene.
Co-Authors A Syahrir, Nur Hilal Apriana Apriana B, Ihsan Hidayatullah, Muhammad Hijrah, Muh. Hikmah Hikmah Kartini Kartini Mahgfirah, Aulya Atika Mayapada, Retno Musafira Musafira Muzakir, Nurul Azizah Nuraviat, Sitti Pardomuan Robinson Sihombing Presda, Ignasius Syahrir, Nur Hilal A. Wahyudi Wahyudi Yanti, Reski Wahyu