Forecasting the Consumer Price Index in Bima City Using Random Forest Regression - Naafia, Suci Faaza; Fathir; mustafidah, Hilyatul (Desimal: Jurnal Matematika, 2026)
Accurate forecasting of the Consumer Price Index (CPI) is essential for supporting regional inflation monitoring and evidence-based economic decision-making. However, city-level CPI forecasting remains limited, particularly in smaller urban areas where local expenditure patterns may differ from national trends. This study aimed to develop a predictive model for the General CPI of Bima City, Indonesia, using Random Forest Regression based on monthly expenditure-group CPI data. A quantitative predictive research design was employed using secondary data published by the Central Statistics Agency (BPS) of Bima City from January 2013 to December 2025, comprising 156 monthly observations. The dataset was divided into training and testing subsets using an 80:20 ratio. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), while feature-importance analysis was performed to identify the most influential expenditure groups. The model achieved an MAE of 0.7626, an MSE of 1.4501, an RMSE of 1.2042, and an R² of 0.994, indicating strong predictive performance on the testing dataset. The actual–predicted comparison and scatter plot demonstrated close agreement between observed and predicted CPI values. Feature-importance analysis identified Finished Food, Health, and Foodstuffs as the dominant predictors contributing to model performance. These findings demonstrate that Random Forest Regression can effectively model the relationship between expenditure-group CPI variables and General CPI within the observed dataset while providing interpretable information regarding variable contribution. This study contributes to the growing application of interpretable machine learning in regional economic forecasting and offers a practical framework for supporting localized CPI monitoring and data-informed inflation analysis.