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All Journal Jurnal Sistem Komputer dan Informatika (JSON) Journal of Technomaterial Physics
Arya Zaki Ramadhan
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Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Comparison of Artificial Neural Network Models for Rainfall Prediction in Palu City Arya Zaki Ramadhan; Febby Debora Abigael; Muhammad Fany Nur Wibowo
Journal of Technomaterial Physics Vol. 7 No. 1 (2025): Journal of Technomaterial Physics
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jotp.v7i1.19757

Abstract

Rainfall prediction is crucial to support natural disaster mitigation and water resource management, especially in areas like Palu City with dynamic rainfall patterns. This study evaluated the performance of three Artificial Neural Network (ANN) models with different architectures to identify the most accurate model in predicting rainfall in 2023. To obtain the model, the historical data of nine meteorological parameters in Palu City from 2018 to 2022 was processed using the Python programming language through pre-processing, processing, post-processing, and verification stages. All three models obtained are designed with hidden layers and different nodes. The best model obtained was Model A with one hidden layer, 8 nodes, and a MAPE value of 9.42%, putting it in the excellent category. Meanwhile, Model B and Model C are in a suitable category with MAPE values of 14.43% and 10.23%. The challenge of using the ANN method in predicting rainfall is its tendency to equalize extreme rain. Therefore, complete data is needed to improve ANN performance.
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 Febby Debora Abigael I Dewa Gede Loka Maheswara Muhammad Fany Nur Wibowo Muhammad Fany Nurwibowo Rica Azzura Maldina Yosik Norman