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All Journal Journal Of Artificial Intelligence And Software Engineering
Wijaya, Muhammad Krisna
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Computer Science & IT

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Predicting Indonesian Inflation Rate Using Long Short-Term Memory (LSTM) Wijaya, Muhammad Krisna; Nastiti, Faulinda Ely; Farida, Anisatul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7178

Abstract

Inflation is a crucial economic indicator that requires an accurate prediction model. This research aims to develop a prediction system for the monthly inflation rate in Indonesia using the Long Short-Term Memory (LSTM) architecture. The method includes historical data acquisition from Bank Indonesia, preprocessing with Min-Max Scaler normalization, and training a univariate LSTM model. Evaluation results show excellent performance with an MAE of 0.2999, an RMSE of 0.3903, and an R² of 0.8796, indicating the model explains 88% of the data's variability. It is concluded that LSTM is effective for inflation forecasting in Indonesia and serves as a solid baseline for future research.
Co-Authors Anisatul Farida Faulinda Ely Nastiti