<![CDATA[Extreme daily rainfall in rapidly urbanizing tropical cities frequently overwhelms drainage and disrupts critical services, yet station-scale forecasting remains limited by convective variability and sparse observations. This motivates lightweight, interpretable machine-learning tools that operate on routine station data. We propose and evaluate a station-scale framework to classify heavy-rainfall days (50 mm) in a humid tropical setting. Using 1,796 daily observations from the Soekarno-Hatta Meteorological Station (20182022), we engineered lag-informed predictors (e.g., previous-day rainfall, 3-day sums/means) and compared three representative classifiers, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Class imbalance was addressed with class-weighted training, and models were assessed on a held-out test set using precision, recall, F1, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC). LR achieved the highest recall (0.429), indicating moderate sensitivity to rare heavy-rainfall events, whereas RF yielded the best probabilistic discrimination (AUC = 0.619) but failed to flag positives at the default threshold; SVM displayed near-random behavior. Feature analyses highlighted humidity, temperature, and recent rainfall accumulation as the most influential predictors, consistent with tropical convective processes. Despite severe class imbalance, simple, station-based classifiers can extract actionable signals for rare-event screening in data-limited tropical regions. Operational value is likely to improve through probability calibration and threshold tuning, ensemble integration, and spatial generalization to multi-station settings.]]>
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