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All Journal Jurnal Pendidikan Matematika (JUDIKA EDUCATION)
Wibowo, Nurdin
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Education Mathematics

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Evaluasi Kinerja Algoritma Random Forest dan KNN untuk Prediksi Cuaca di Jakarta Wibowo, Nurdin; Hardyanto, Wahyu; Sepriando, Alpon; Djuniadi, Djuniadi; Nurmayati, Tri Nurmayati; Alam, Fakhrul
Jurnal Pendidikan Matematika : Judika Education Vol. 9 No. 3 (2026): Jurnal Pendidikan Matematika:Judika Education
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/wzj0r898

Abstract

This study aims to compare the performance of Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying daily weather categories at Kemayoran Meteorological Station, Jakarta. The data used were BMKG observation data from 2017 to 2023, with classification targets consisting of No Significant Weather, RA, TS, and TS,RA. The variables included temperature, rainfall, air pressure, humidity, wind speed, sunshine duration, wind direction, and month. The data were preprocessed and divided into training and testing sets using an 80:20 ratio. The results showed that Random Forest achieved an accuracy of 78% with a weighted average F1-score of 0.75, while KNN achieved an accuracy of 65% with a weighted average F1-score of 0.59. Random Forest performed better in classifying dominant weather categories, although both algorithms still had limitations in identifying minority categories, particularly TS. These findings indicate that Random Forest is more suitable for daily weather classification based on BMKG observation data in urban areas.   Keywords: K-Nearest Neighbors; Random Forest; Weather Classification
Co-Authors Alam, Fakhrul Djuniadi Djuniadi Nurmayati, Tri Nurmayati Sepriando, Alpon Wahyu Hardyanto