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All Journal Journal of Engineering Technology and Science Integration (JETSI)
Kania Laksono
Universitas Islam Negeri Raden Mas Said Surakarta, Indonesia

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The Use of Machine Learning and Ensemble Stacking for Classifying Rainfall Intensity in Central Jakarta in 2025 Damar Januar Jorgie Marseno; Maysya Amelia Putri; Muhammad Favian Gustaf Ahnaf; Muhammad Jawahir Asyrof; Kania Laksono; Faridhatun Nikmah
Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration
Publisher : PT. Nordeo Scholar Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68627/jetsi.v1i1.10

Abstract

High rainfall intensity in Central Jakarta can trigger inundation and flooding that disrupt infrastructure, mobility, and public activities. This condition highlights the need for a reliable rainfall classification model. This study develops a next-day rainfall classification approach using a Stacking Ensemble that combines Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost as base models, with LassoCV as the meta-model. The dataset was obtained from BMKG meteorological records for Central Jakarta in 2025. The research process follows the CRISP-DM framework, covering data understanding, data preparation, feature engineering, modeling, and evaluation. From 365 daily observations, the feature engineering stage produced 34 derived features. The dataset was then split into 80% training data and 20% testing data according to chronological order, while model validation was conducted using 5-fold Time Series Cross Validation. The evaluation results indicate that Stacking Lasso achieved the best performance, with an F1-Score of 0.774 and a recall of 0.878. During the weighting process, LassoCV retained only LightGBM as the active contributor, with a coefficient of 0.2671. SHAP interpretation showed that average relative humidity was the most influential variable in predicting rainfall. These findings suggest that the Stacking Ensemble approach has potential as a foundation for developing an urban hydrometeorological disaster mitigation support system.
Co-Authors Damar Januar Jorgie Marseno Faridhatun Nikmah Maysya Amelia Putri Muhammad Favian Gustaf Ahnaf Muhammad Jawahir Asyrof