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All Journal Jurnal Ilmiah Pengabdian dan Inovasi JUSTIFY : Jurnal Sistem Informasi Ibrahimy COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Horizon: Indonesian Journal of Multidisciplinary JAR'S (Journal of Advanced Research in Informatics) Edusight Jurnal Pengabdian Masyarakat (EduJPM) Journal of Artificial Intelligence
Beny Yusman
Universitas Hafshawaty Zainul Hasan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Transportation Other

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Rainfall Prediction in East Java Using Attention-Based LSTM Beny Yusman
Khatulistiwa SMART: Science, Methodology, Artificial intelligence, Research, and Technology Vol 3 No 1 (2026): June 2026
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/kjarti.v3i1.675

Abstract

Rainfall is one of the most influential meteorological parameters affecting agriculture, transportation, infrastructure, public health, and disaster mitigation. Accurate rainfall prediction is highly important, especially in East Java, one of the largest rice-producing regions in Indonesia. This study aims to develop a daily rainfall prediction model using the Attention-Based Long Short-Term Memory (LSTM) method. The dataset used in this study was obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG), covering the period from January 1, 2000, to December 31, 2023, with a total of 8,764 daily records. The variables include average temperature, relative humidity, rainfall, sunshine duration, and average wind speed. The research stages include data collection, preprocessing, missing value handling, outlier replacement using the Z-Score method, correlation-based feature selection, Min-Max normalization, lagged feature construction, data splitting, Attention-Based LSTM model design, model training, model testing, and Streamlit application integration. The best model was obtained using a batch size of 16 and 100 epochs, achieving a validation MSE of 0.007038 and a validation RMSE of 0.083893. On testing data, the model achieved an MSE of 0.008076 and an RMSE of 0.089871. These results indicate that Attention-Based LSTM can model daily rainfall patterns effectively and has potential as a data-driven rainfall prediction support system
Co-Authors Adi Putra Darmawan Bambang Darmawan, Adi Putra Furqan, Moh. Isyvina Unaizah Royya Khairi, Matlubul M. Syafiih Moh. Fadel Moh. Furqan Muafi Nadiyah Nadiyah Nadiyah Putri, Merlina Eka Rania Majdoubi Saiful Rizal Sri Nurhasana Syafiih, M . Ummi Latifah Zainal Arifin