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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Building of Informatics, Technology and Science
Hermawan, Taufan
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Computer Science & IT

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Journal : Building of Informatics, Technology and Science

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Perbandingan Metode Recurrent Neural Network (RNN) dan Long Short-Term Memory (LSTM) untuk Prediksi Curah Hujan Hermawan, Taufan; Zuliarso, Eri
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8099

Abstract

The increase in extreme rainfall intensity due to climate change has caused Batang Regency to become a hydrometeorological disaster-prone area. This research aims to build an day rainfall prediction model using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) based on BMKG historical data. The model is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The results show that LSTM has higher accuracy than RNN, with an RMSE: 0.1036 | MAE: 0.0730. Meanwhile, RNN obtained an RMSE: 0.1035 | MAE: 0.0763. LSTM is also more stable in predicting temperature, direction, and wind speed variables. These findings show that LSTM is more effective for weather time series data and can be used as a basis for developing data-based disaster early warning systems in local areas.
Co-Authors Afandi , Afandi Budiarto, Indri Dian Kristiawan Nugroho Eri Zuliarso Rokhayadi, Wakhid