Forecasting Consumer Price Index in Personal Care Sector in Bukittinggi Using SVR with Grid Search and Radial Basis Function Kernel - khairunnisa Pane; Fadhilah Fitri; Dina Fitria (UNP Journal of Statistics and Data Science, 2025)
Inflation, measured by the Consumer Price Index (CPI), is vital for economic stability and policy making. In Bukittinggi, the Personal Care and Other Services sector shows notable CPI fluctuations, complicating accurate forecasting. This study uses Support Vector Regression (SVR) to predict monthly CPI data for this sector from 2020 to 2024. Data from Statistics Indonesia was normalized with Min-Max normalization to improve model accuracy and avoid scale distortion. Lag features were added to capture time dependencies, and data was split into training (80%) and testing (20%) sets. A linear SVR model was first applied but showed limited success due to the data’s non-linear nature. Therefore, the Radial Basis Function (RBF) kernel was used, with hyperparameters (C, sigma, epsilon, folds) optimized via Grid Search and cross-validation. The optimal settings (C=32, sigma=2, epsilon=0.1, k=10) yielded the lowest RMSE of 0.1099 in cross-validation and 0.0767 on testing. Results demonstrate that the RBF-SVR model effectively captures non-linear CPI patterns and outperforms the linear model. Evaluation metrics included RMSE, MSE, and MAE. The study concludes that SVR combined with Grid Search offers a robust forecasting method for sectors with complex CPI behavior, supporting local economic planning in Bukittinggi. Future research could investigate hybrid models and larger datasets to enhance prediction accuracy and adaptability to market changes.