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All Journal JMM (Jurnal Masyarakat Mandiri) TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Community Empowerment Jurnal Teknologi dan Sistem Tertanam Jurnal Data Mining dan Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika: Jurnal Pengembangan IT Journal of Applied Informatics Science
Ardi Susanto
Department Of Informatics, Politeknik Harapan Bersama, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences Other

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Integration of Particle Swarm Optimization in Bidirectional Memory Networks for Improved Daily Climate Forecasting Accuracy Ardi Susanto; Heru Purnomo Kurniawan; Lia Farhatuaini; Muhammad Iszul Wilsa; Gina Khayatun Nufus; Ananda Sathria Maulana Amri
Journal of Applied Informatics Science Volume 1 Issue 2 (2025)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v1.i2.85

Abstract

Global climate change has caused rainfall patterns to become increasingly fluctuating and difficult to predict using conventional weather forecasting methods. Accurate daily rainfall prediction is crucial for hydrometeorological disaster mitigation and agricultural sector planning. Although Deep Learning models such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) are capable of handling complex time-series data, the manual determination of hyperparameters often results in suboptimal models and entrapment in local optima. This study proposes the integration of the Particle Swarm Optimization (PSO) algorithm to automatically optimize hyperparameters (number of hidden neurons, dropout rate, and learning rate) in LSTM and BiLSTM architectures. The models were evaluated using a multivariate daily climate observation dataset encompassing temperature, humidity, wind speed, and actual rainfall. Experimental results indicate that PSO-based optimization significantly enhances prediction performance compared to baseline models. The PSO-LSTM approach successfully reduced the Root Mean Square Error (RMSE) to 17.59 mm and Mean Absolute Error (MAE) to 9.07 mm, comparable to the performance of PSO-BiLSTM, which achieved an RMSE of 17.59 mm and an MAE of 9.20 mm. These findings prove that automatic parameter tuning using swarm intelligence algorithms can highly optimize sequential neural network architectures in capturing rainfall pattern volatility, making it highly recommended as a foundation for a more accurate early warning system.
Co-Authors Aldi Budi Rianta Ananda Sathria Maulana Amri Arief Rachman Dairoh Dairoh Dairoh Dairoh, Dairoh Dedy Agung Prabowo Dega Surono Wibowo Dega Surono Wibowo Dwi Intan Af’idah Dyah Apriliani Elang Bimantoro Eli Krisnawati Eri Sasmita Susanto Fahri Hamdani Farhatuaini, Lia Gina Khayatun Nufus Ginanjar Wiro Sasmito Juniardi Akhir Putra Kurniawan, Heru Purnomo M Nishom M Yoka Fathoni M. Nishom Muhammad Fikri Hidayattullah Muhammad Iszul Wilsa Muhammad Rizky Musnadil Firdaus Nabila Oper Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Sandhy Fernandez Sharfina Febbi Handayani W, Yunanri Waliulu, Raditya Faisal Yasinta Bella Fitriana Yustia Hapsari