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All Journal AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Jurnal Sistem Komputer dan Informatika (JSON)
Yosik Norman
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Computer Science & IT Control & Systems Engineering Mathematics

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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
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol. 13 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

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Abstract

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.
Perbandingan Performa Multi-Algoritma Machine Learning dengan Dua Strategi Validasi pada Klasifikasi Curah Hujan I Dewa Gede Loka Maheswara; Arya Zaki Ramadhan; Rica Azzura Maldina; Muhammad Fany Nurwibowo; Yosik Norman
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9475

Abstract

Prediksi curah hujan yang akurat masih menjadi tantangan karena kompleksitas proses atmosfer serta dampaknya terhadap berbagai sektor. Performa algoritma machine learning dalam klasifikasi curah hujan sangat dipengaruhi oleh karakteristik data dan metode validasi, sehingga diperlukan evaluasi komparatif untuk menentukan model yang paling sesuai pada konteks lokal. Penelitian ini bertujuan membandingkan performa lima model machine learning, yaitu Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbor, dan Decision Tree dalam klasifikasi curah hujan di Kabupaten Tapanuli Tengah menggunakan data observasi harian periode 2015–2024 sebanyak 32.796 data yang diperoleh dari Stasiun Meteorologi FL Tobing. Evaluasi dilakukan melalui skema pembagian data dan 10-cross fold validation dengan metrik precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa Random Forest secara konsisten memberikan performa terbaik pada kedua skema validasi dengan f1-score sebesar 62% dan 63%, lebih stabil dibandingkan model lainnya pada kondisi distribusi kelas yang tidak seimbang. Temuan ini menunjukkan bahwa pendekatan ensemble lebih adaptif dalam menangkap hubungan nonlinier parameter meteorologi serta memberikan dasar metodologis dalam pemilihan model klasifikasi curah hujan untuk mendukung mitigasi bencana hidrometeorologi.
Co-Authors Alamin, Mirza Virgiansah Arya Zaki Ramadhan I Dewa Gede Loka Maheswara Juang Merdeka Muhammad Fany Nurwibowo Rica Azzura Maldina Syahrin, Khairummin Alfi Tiara Emanuella Disera