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All Journal Jurnal Ilmiah Aplikasi Isotop Radiasi WAPFI (Wahana Pendidikan Fisika) Spektra: Jurnal Fisika dan Aplikasinya Jurnal ULTIMA Computing Indonesian Journal of Biomedicine and Clinical Sciences Indonesian Journal of Aerospace
Adhi Harmoko Saputro
Department Of Geography, Faculty Of Mathematics And Natural Sciences, University Of Indonesia, UI Depok Campus, Depok, 16424, Indonesia
Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Physics Social Sciences Other

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Journal : Jurnal ULTIMA Computing

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Long Term Prediction of Extreme Weather with Long Short Term Memory (LSTM) Model: Effect of Climate Change Putri, Nurulita Purnama; Harmoko Saputro, Adhi
ULTIMA Computing Vol 17 No 1 (2025): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v17i1.4022

Abstract

Increasingly intense climate change has increased the frequency and intensity of extreme weather, making weather prediction critical for mitigation and adaptation. This research focuses on long-term prediction of extreme weather using the Long ShortTerm Memory (LSTM) model, as well as evaluating the influence of climate change on prediction accuracy. In this study, historical weather data is used to train and test an LSTM model combined with a RandomForestClassifier. Analysis was carried out using the Mean Squared Error (MSE) evaluation technique for 50 epochs and 8 trials at various threshold values (26, 29, 32, 35, 38, 41, 44, 47). The research results show that the LSTM model is able to predict extreme weather with an accuracy of up to 100%. Apart from that, this research also predicts daily rainfall in Bandung City through the process of data collection, preprocessing, normalization and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). This model produces an RMSE of 4.24 and MAE value of 2.72%, indicating quite good predictions. It is hoped that this research can make a significant contribution to the field of meteorology and can be developed further by adding parameters or other methods to improve the quality of predictions. Suggestions are given to increase the amount of data used to obtain better prediction results in the future.
Co-Authors Annisa, Risa Bayu Azmi Dimyati, Ratih Dewanti Dony Kushardono Handayani, Windri Irene Darmawan Kustiyo Kustiyo, Kustiyo LILIK BUDIPRASETYO Miftahul Jannah Panggabean, Prima Ria Rumata Pramesti, Hari Agusasi Putri, Nurulita Purnama Rista, Rista Rokhmatuloh, Rokhmatuloh Sampurna, Adhimukti T. Sartika Salaswati Shannaz Nadia Yusharyahya Titik Lestari Wahyudi Hasbi Wibisono Wibisono