<![CDATA[Estimating rainfall accurately is crucial for both the community and various institutions involved in managing water resources and preventing disasters. The XGBoost model has demonstrated its effectiveness in predicting rainfall, but it still requires fine-tuning of hyperparameters to enhance its performance. This study seeks to determine the optimal learning rate for rainfall prediction while keeping the max_depth and n_estimator parameters fixed. The hyperparameter optimization process was carried out using a two-step approach: an initial coarse search using RandomizedSearchCV followed by a more detailed fine-tuning using GridSearchCV. The model's foundation relied on historical rainfall data gathered over three months from the Automated Weather Observed System (AWOS) at the Pontianak Meteorological Station, recorded on an hourly basis. To assess the model's performance, several metrics were employed, including accuracy, precision, recall, F1 score, and ROC-AUC. The model demonstrated promising results, with accuracy, precision, recall, and F1 score all reaching 95%, indicating its ability to effectively predict rainfall. However, the ROC-AUC score was somewhat lower at 62%. After conducting the hyperparameter search, the optimal learning rate determined for the model, utilizing the 2040 dataset, was found to be 0.204.]]>
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