KLASIFIKASI CURAH HUJAN DI KOTA MAKASSAR MENGGUNAKAN GRADIENT BOOSTING MACHINE (GBM)
Abstract
Rainfall is one of the important parameters in determining the climate of an area. Makassar, as one of the largest cities in Indonesia, has varying rainfall patterns throughout the year. This research aims to classify rainfall in Makassar City using the Gradient Boosting Machine (GBM) method. The secondary data used in this study were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG), with predictor variables including wind speed, humidity, and air temperature, and the target variable being rainfall category, consisting of no rain, very light rain, light rain, moderate rain, heavy rain, and very heavy rain. To address class imbalance in the data, this study uses the Random Undersampling (RUS) technique. The GBM model with optimal hyperparameter configuration (n_estimators, learning_rate, max_depth, subsample, min_samples_leaf, max_features) achieved a classification accuracy rate of 98.46%, precision of 93%, recall of 98%, and F1-score of 95% with a training and testing data split of 80:20. The research results show that the GBM method is able to classify rainfall very well and can be used as a tool to assist in disaster mitigation planning and water resource management in Makassar City. 95% pada proporsi data pelatihan dan pengujian 80:20. Hasil penelitian menunjukkan bahwa metode GBM mampu mengklasifikasikan curah hujan dengan sangat baik dan dapat digunakan sebagai alat bantu dalam perencanaan mitigasi bencana serta pengelolaan sumber daya air di Kota Makassar.
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