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All Journal JURNAL PASTI (PENELITIAN DAN APLIKASI SISTEM DAN TEKNIK INDUSTRI) JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal IUS (Kajian Hukum dan Keadilan) Jurnal Pengabdian Kepada Masyarakat Patikala Journal of Innovation Research and Knowledge Jurnal Manajemen dan Pendidikan Agama Islam International Journal of Informatics Engineering and Computing
Selamet Riadi
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Transportation

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Journal : International Journal of Informatics Engineering and Computing

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Enhancing Rainfall Prediction Using LSTM Algorithm Selamet Riadi; Jamil, Trisna
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.86

Abstract

Rainfall is an important factor that influences various aspects of human life, including agriculture, transportation, and urban planning. With climate change, the need for accurate rainfall prediction systems is becoming increasingly urgent. Traditional methods, such as statistical or physical models, often struggle to deal with the complex and nonlinear nature of weather data. This research proposes the use of Long Short-Term Memory (LSTM), a deep learning model capable of processing sequential data, to predict rainfall based on historical data. The model can capture long-term dependencies, making it suitable for analyzing meteorological data such as temperature, humidity, wind speed and rainfall intensity. This paper investigates the performance of an LSTM-based rainfall prediction system, and compares it with traditional forecasting methods. Evaluation metrics such as Root Mean Square Error (RMSE) are used to assess the accuracy of predictions. These findings indicate that LSTM-based models provide a more reliable solution for rainfall prediction, especially in detecting extreme weather events early.
Co-Authors Ahmad Sam’un Bhira Muslim Marathony Dimas Novrisal Eka Daryanto Hendri Hendri Idham Idham Idham Jamil, Trisna Lalu Surya Jagat Muhamad Aris Raharjo Resa Taruna Suhada Runi Fazalani Yundari, Yundari