Deep Learning-Based Weather Prediction Model Using BMKG Big Data for Rice Planting Season Optimization in Indonesia

Authors

  • Maradona Jonas Simanullang Universitas Senior Medan
  • Elsya Sabrina Asmita Simorangkir Faculty of Technology and Health Sciences, Universitas Senior Medan, Indonesia
  • Herry Daniel Marpaung Faculty of Technology and Health Sciences, Universitas Senior Medan, Indonesia
  • Yudisa Halawa Faculty of Technology and Health Sciences, Universitas Senior Medan, Indonesia
  • Tria Adelia Putri Br Gurusinga Faculty of Technology and Health Sciences, Universitas Senior Medan, Indonesia
  • Frans Mikael Sinaga Informatics Department, Faculty of AI and Data Sciences, Universitas Pelita Harapan, Indonesia

DOI:

10.33395/sinkron.v10i4.16703

Keywords:

Rainfall Prediction, Deep Learning, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), BMKG Big Data, Time Series Forecasting, Rice Planting Season Optimization.

Abstract

This study develops and evaluates a deep learning-based weather prediction model built on BMKG (Indonesian Meteorological, Climatological, and Geophysical Agency) data, with the aim of informing rice planting season optimization in Deli Serdang Regency, North Sumatra, Indonesia. Two recurrent architectures, Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), were compared for daily rainfall prediction using 788 observations collected between January 2024 and March 2026, split chronologically into training, validation, and testing subsets. The LSTM model achieved a Root Mean Square Error (RMSE) of 24.42 mm, a Mean Absolute Error (MAE) of 15.20 mm, and an R² of 0.0420, while the BiLSTM model achieved a lower RMSE of 23.64 mm, a lower MAE of 14.89 mm, and a lower R² of 0.0233. Although BiLSTM produced marginally smaller errors, LSTM explained a slightly larger share of rainfall variability, and both R² values remained close to zero, indicating that neither model captured the full variability of the rainfall series. Because of its higher R², the LSTM-based forecasts were translated into an indicative cropping-calendar classification of wet, normal, and dry months as a preliminary basis for advancing, maintaining, or delaying the onset of the rice planting season in Deli Serdang Regency. Given the limited explanatory power of both models and the absence of baseline forecasts, repeated training, and time-series cross-validation in this study, these planting-season indications should be read as exploratory and complementary to, rather than a replacement for, the operational cropping calendar issued by BMKG and the Ministry of Agriculture. Future research should incorporate longer historical records, additional meteorological predictors, baseline models, and rigorous validation to develop more reliable, data-driven rice planting season decision support for Indonesia.

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How to Cite

Simanullang, M. J. ., Simorangkir, E. S. A. ., Marpaung, H. D. ., Halawa, Y. ., Br Gurusinga, T. A. P. ., & Sinaga, F. M. . (2026). Deep Learning-Based Weather Prediction Model Using BMKG Big Data for Rice Planting Season Optimization in Indonesia. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(4), 2050-2061. https://doi.org/10.33395/sinkron.v10i4.16703

Issue

Vol. 10 No. 4 (2026): Research Articles October 2026

Section

Articles

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Copyright (c) 2026 Maradona Jonas Simanullang, Elsya Sabrina Asmita Simorangkir, Herry Daniel Marpaung, Yudisa Halawa, Tria Adelia Putri Br Gurusinga, Frans Mikael Sinaga

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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