Enhanced Stacked GRU Model for Monthly Rice Production Forecasting in Bali Province

Authors

  • I Gusti Agung Raditiya Gotama Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia
  • I Gede Iwan Sudipa Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia
  • Anak Agung Gede Raka Wahyu Brahma Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia
  • Made Suci Ariantini Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia
  • Dewa Ayu Putri Wulandari Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia

DOI:

10.33395/sinkron.v10i1.15715

Keywords:

Deep Learning, Food Security, Forecasting, Gated Recurrent Unit (GRU), Stacked GRU

Abstract

Rice production has a seasonal pattern that depends on the planting cycle and environmental conditions, requiring forecasting methods that can accurately model temporal dynamics. This study aims to predict monthly rice production in Bali Province using the Stacked Gated Recurrent Unit (GRU) architecture. Monthly rice production data from 2018 to 2024 from the Central Statistics Agency (BPS) was used as the main source. The preprocessing stage includes data cleaning, Min-Max normalization, and feature engineering in the form of creating sin_month and cos_month features to capture seasonal patterns, as well as a 3-month rolling mean to extract short-term trends. The proposed stacked design with dual-layer GRU combined with seasonal features improves temporal pattern extraction compared to single-layer GRU baselines. The model was tested using three configurations, and Scheme 3 provided the best performance with an MAE value of 1610.21, an RMSE of 2055.90, and a MAPE of 14.29%, which is considered good accuracy. The model was able to follow seasonal production trends, including an increase at the beginning of the year and a decrease during the planting period. Long-term predictions for the next 12 months and quarterly forecasts per district/city also showed patterns consistent with historical data. The results of the study indicate that Stacked GRU is effective in forecasting seasonal rice production and can be used as a basis for decision support in food security planning in Bali.

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How to Cite

Gotama, I. G. A. R., Sudipa, I. G. I., Brahma, A. A. G. R. W. ., Ariantini, M. S. ., & Wulandari, D. A. P. . (2026). Enhanced Stacked GRU Model for Monthly Rice Production Forecasting in Bali Province. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(1), 652-664. https://doi.org/10.33395/sinkron.v10i1.15715

Issue

Vol. 10 No. 1 (2026): Article Research January 2026

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Articles

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Copyright (c) 2026 I Gusti Agung Raditiya Gotama, I Gede Iwan Sudipa, Anak Agung Gede Raka Wahyu Brahma, Made Suci Ariantini, Dewa Ayu Putri Wulandari

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