Forecasting Indonesian Goods and Services Imports Using Machine Learning: A Comparative Evaluation of Model Performance

Accurate forecasting of import values is crucial for effective economic planning and policy-making in emerging economies like Indonesia. Traditional forecasting methods often face challenges in capturing the complex, non-linear dynamics inherent in macroeconomic time series data. This study evaluates the performance of three prominent Machine Learning (ML) models—Support Vector Regression (SVR), Random Forest, and Decision Tree—for forecasting Indonesian goods and services imports. Utilising historical macroeconomic time series data for Indonesia spanning 1970–2023, the models were trained and rigorously evaluated using standard metrics, including mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (R ² ). To address the limitation of the relatively small original dataset size, data augmentation via linear interpolation was explored, and the models' prediction accuracy for the year 2023 was specifically assessed. The results indicate that SVR demonstrated superior performance compared to Random Forest and Decision Tree based on the evaluation metrics and achieved the highest accuracy in predicting the 2023 import value, particularly after data interpolation was applied, which generally improved point prediction accuracy. The findings suggest that ML models, especially SVR, are effective and promising tools for enhancing the precision of Indonesian import forecasting. This research provides valuable empirical evidence for policymakers and practitioners seeking to leverage advanced computational techniques for improved economic forecasting and planning in an emerging market context while also highlighting considerations related to data characteristics and augmentation strategies for future methodological advancements.