An Interpretable Machine Learning Approach for Inflation Forecasting in Indonesia Using Domestic Macroeconomic Indicators

Accurate inflation forecasting is essential for supporting forward-looking monetary policy, maintaining price stability, and preserving economic welfare. This study proposes an interpretable machine learning framework for inflation forecasting in Indonesia by integrating domestic macroeconomic indicators and seasonal variables. The study employs Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) models using monthly data from 2008 to 2025. To systematically evaluate model performance, three experimental scenarios are implemented: baseline modeling, time-series feature augmentation, and hyperparameter optimization. Model performance is evaluated using RMSE, MAE, and R² metrics. The results consistently show that ensemble-based methods outperform ANN across all scenarios, with XGBoost achieving the best overall performance after temporal feature augmentation and hyperparameter optimization (RMSE = 0.4987, MAE = 0.3339, R² = 0.9517). The findings further indicate that temporal feature augmentation provides relatively limited improvement, whereas hyperparameter optimization substantially enhances forecasting accuracy. SHAP analysis identifies money supply (M2) and wages as the dominant contributors to inflation predictions, suggesting that monetary liquidity and labor-related factors play a more significant role than seasonal patterns in explaining inflation dynamics. Rather than contributing through algorithmic novelty, this study contributes through a systematic and interpretable forecasting framework that integrates predictive accuracy and explainable artificial intelligence to support transparent and policy-relevant inflation analysis in Indonesia and other emerging economies with similar economic structures.