Predicting Indonesian Inflation Rate Using Long Short-Term Memory (LSTM) - Wijaya, Muhammad Krisna; Nastiti, Faulinda Ely; Farida, Anisatul (Politeknik Negeri Lhokseumawe, 2025)
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.