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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization Jiko (Jurnal Informatika dan komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Khazanah Pendidikan Islam International Journal of Islamic Khazanah Journal of Applied Data Sciences Jurnal Algoritma Jurnal Informatika Polinema (JIP)
Maylawati, Dian Sa'adillah
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Public Health Social Sciences Other

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Feature Engineering for Tropical Rainfall Forecasting Using Random Forest and Support Vector Regression Slamet, Cepy; Imron, Rizka M; Wahana, Agung; Maylawati, Dian Sa'adillah; Zulfikar, Wildan Budiawan; Ramdhani, Muhammad Ali
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1111

Abstract

The complex dynamics of weather variability in Indonesia, influenced by multiple climatic drivers, make rainfall forecasting in tropical regions a significant scientific challenge. This study proposes an automated feature engineering pipeline to enhance the performance of Random Forest Regression (RFR) and Support Vector Regression (SVR) models for tropical rainfall prediction. Monthly rainfall data spanning 388 months (1993–2025) from a BMKG station were used as the basis for model development. The pipeline systematically generates temporal, seasonal, statistical, and anomaly-based features to provide domain-informed representations for non-sequential learning algorithms. Model performance was evaluated under four temporal data partitioning scenarios using R², RMSE, and probabilistic confidence intervals derived from bootstrap residual simulations. Results indicate that RFR achieved the highest predictive accuracy (R² = 0.93; RMSE = 31.01 mm) and demonstrated superior temporal–seasonal stability (Rolling CV: R² = 0.81 ± 0.07; RMSE = 55.44 ± 16.18), with comparable performance between wet and dry seasons. Conversely, SVR showed greater sensitivity to seasonal variability, with R² dropping to 0.55 during the wet season, indicating higher uncertainty under extreme rainfall conditions. Robustness and drift analyses further revealed that RFR adapts better to temporal and seasonal shifts, while SVR remains relevant as an adaptive model for extreme risk analysis. Overall, this study contributes to the development of automated feature engineering, reproducible climatological forecasting pipelines, and probabilistic modeling frameworks for rainfall prediction under uncertainty in tropical regions.
Co-Authors Agung Wahana Barzan Faizin Cepy Slamet Diena Rauda Ramdania Enjang AS, Enjang Fatonah, Fany Risti Fitri, Susanti Ainul Harahap, Akbar Hidayatullah Hartawan, Gaduh Herdiyanto, Reza Fahlevi Ichsan Budiman Imron, Rizka M Kasmin, Fauziah Kholiq, Achmad Khosyi'ah, Siah Kumar, Yogan Jaya Mi’raj Fuadi Muhammad Ali Ramdhani Muhammad Insan Al-Amin Muharam, Aldi Fahluzi Nugraha, Rizky Rahmat Nur Lukman Nurlatifah, Eva Rahman, Titik Khawa Abdul Rizqullah, Naufal Rohmat Mulyana Sapdi Saputra, Muhammad Indra Nurardy Syahrifudin, Umar Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu Yana Aditia Gerhana, Yana Aditia Yniarto, Kurniawan Yuhendra AP