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All Journal FORUM STATISTIKA DAN KOMPUTASI Jurnal Gaussian Jurnal Media Infotama Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan IJID (International Journal on Informatics for Development) Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Diophantine Journal of Mathematics and Its Applications
Cici Suhaeni
Statistics And Data Science, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Analysis and Optimization of Rainfall Prediction in Makassar City Using Artificial Neural Networks Based on Data Augmentation, Regularization, and Bayesian Optimization Adib Roisilmi Abdullah; Kusman Sadik; Cici Suhaeni; Agus Muhammad Saleh
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8304

Abstract

This study develops a robust and efficient rainfall prediction model using an Artificial Neural Network (ANN), significantly enhanced through integrated data augmentation, regularization, and Bayesian optimization techniques. We utilized a dataset of 118 monthly rainfall records from Makassar City, spanning 2014–2022, sourced from the Meteorological, Climatological, and Geophysical Agency (BMKG). To effectively capture inherent temporal patterns, lag features (specifically lag-1, lag-3, and lag-6 rainfall values) were meticulously constructed as input variables. Subsequently, Min-Max normalization was applied across all features, ensuring input consistency and optimizing the ANN's learning process. An initial manual grid search identified the most effective baseline ANN architecture, featuring four hidden layers ([128, 32, 16, 64] neurons), a tanh activation function, and a learning rate of 0.01. While the baseline ANN model achieved a commendable initial performance with an RMSE of 0.1608, comprehensive experiments revealed the superior benefits of a fully integrated approach. This advanced model, which synergistically combined data augmentation (to address data limitations and enhance generalization), regularization (to mitigate overfitting), and Bayesian optimization (for efficient hyperparameter tuning), demonstrated significantly improved generalization capabilities and enhanced model stability. This integrated model yielded an RMSE of 0.1861, an MSE of 0.0346, and an MAE of 0.1359. These compelling findings unequivocally underscore that integrated optimization strategies are crucial for developing more robust and reliable ANN-based rainfall prediction models, particularly for critical applications in climate-based time series forecasting.
Co-Authors Adib Roisilmi Abdullah Agus M Soleh Agus M. Soleh Agus Mohamad Soleh Agus Muhammad Saleh Anang Kurnia Anik Djuraidah Ayu Annisa Rahmah Bagus Sartono Bimandra Adiputra Djaafara Bimandra Adiputra Djaafara Budi Susetyo Eka Setiawaty Fani Fahira Fani Fahira Farit Mochamad Afendi Fitrianto, Anwar Gerry Alfa Dito Gita Lestari Hari Wijayanto Hwan-Seung Yong I Made Sumertajaya Itasia Dina Sulvianti Jefita Resti Sari Kesuma Millati Kusman Sadik La Ode Abdul Rahman Laily Nissa Atul Mualifah Muhammad Shalih Muhammad Yusran Naufalia Alfiryal Noer Endah Islami Panji Lokajaya Arifa Pika Silvianti Rahardiantoro, Septian Rere Kautsar Ristiyanti Ristiyanti Sabrina Adnin Kamila Sabrina Adnin Kamila Uswatun Hasanah Utami Dyah Syafitri Yuli Eka Putri