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All Journal Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Adyatma, Wimala Eka
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Education Engineering Industrial & Manufacturing Engineering

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FORECASTING INDONESIAN INFLATION: A RECURRENT NEURAL NETWORK-BASED DEEP LEARNING APPROACH Adyatma, Wimala Eka; Windhani, Kikin
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 5 (2026): Volume 10, Nomor 5, October 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i5.53969

Abstract

Forecasting models in the economic field have traditionally relied on conventional statistical methods. However, these methods often face limitations when dealing with volatile and non-stationary data, such as inflation in Indonesia. A deep learning approach based on Recurrent Neural Networks (RNN) is proposed as an alternative for forecasting Indonesian inflation. This study aims to evaluate the accuracy of the RNN method as a forecasting model for inflation in Indonesia. The study employs a quantitative time-series forecasting design using secondary monthly inflation data from January 2003 to January 2026, collected from Bank Indonesia. The analytical framework uses an RNN-based deep learning model with hyperparameter optimization, Min-Max Scaling, and a chronological 70:30 train-test split. Model performance is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the optimal forecasting performance is achieved using the Tanh activation function, 10 RNN units, and a dropout rate of 0.05, with an RMSE of 0.369869 and an MAE of 0.266443 on the test data. The RNN model projects a stable inflation trend ranging from 3.38% to 3.9475%. These findings suggest that RNN can serve as a complementary approach for forecasting Indonesian inflation.
Co-Authors Windhani, Kikin