PERFORMANCE COMPARISON OF RANDOM FOREST, DECISION TREE, AND EXTRA TREES MODELS FOR RAINFALL PREDICTION IN JAKARTA - Syahrin, Khairummin Alfi; Tiara Emanuella Disera; Juang Merdeka; Alamin, Mirza Virgiansah; Yosik Norman (Universitas Ahmad Dahlan, 2026)
Accurate rainfall prediction is important for weather monitoring and flood management in urban areas. This study evaluates the performance of three decision tree-based models, Random Forest, Extra Trees, and Decision Tree, for predicting daily rainfall in Jakarta using data from the BMKG Tanjung Priok observation station for the year 2025. The dataset, expressed in millimeters per day, was preprocessed to handle missing values and ensure consistency, and analyzed using the PyCaret library in the Jupyter Notebook environment. Model training was conducted with optimized hyperparameters, and performance was assessed using MASE, RMSSE, MAE, RMSE, SMAPE, and R². All models produced similar overall trends, although the Extra Trees model showed slightly higher fluctuations. Comparative evaluation indicated that the Random Forest model achieved the best performance, with MASE of 0.7925, RMSSE of 0.6373, MAE of 9.51 millimeters, RMSE of 14.06 millimeters, SMAPE of 1.8287, and R² of -0.8227, demonstrating superior accuracy in capturing rainfall patterns. These results suggest that Random Forest is the most suitable model for daily rainfall forecasting in Jakarta, providing reliable predictions that can support meteorologists and policymakers in improving forecast accuracy and planning.