<![CDATA[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.]]>
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