IMPLEMENTASI ALGORITMA EXTRA TREES UNTUK KLASIFIKASI CUACA PROVINSI DKI JAKARTA DENGAN OVERSAMPLING SMOTE - Raihan Kemmy Rachmansyah (2023)
Weather is the state of the air in a certain area for a limited period of time. The weather in Indonesia, especially in DKI Jakarta Province, is very erratic and difficult to predict. Weather that is difficult to predict disrupts many activities of DKI Jakarta Province residents, so a technological science is needed to classify weather. Therefore, this research applies the Machine Learning method for weather classification in DKI Jakarta Province using the Extra Trees algorithm with the SMOTE oversampling method. The data used in this research is DKI Jakarta Province Weather Forecast data from 2017 to 2018, obtained from the site https://data.jakarta.go.id/. The data obtained has an unbalanced data distribution, so it needs to be balanced first using the data resampling method using the Synthetic Minority Oversampling Technique, or SMOTE. Based on the research results, the SMOTE oversampling method does not affect the evaluation results on the weather classification of DKI Jakarta Province for the better. The best evaluation results were obtained by the model using the Extra Trees algorithm without the SMOTE oversampling method at a ratio of 80% training data and 20% test data, with an accuracy value of 79,8%, precision of 63,1%, and recall of 56,1%.