The Influence of The COVID-19 Pandemics in Indonesia On Predicting Economic Sectors

Abstract

Several studies have tried to prove the link between the economic sectors in Indonesia with the COVID-19 pandemic. However, research has yet to observe the influence of the COVID-19 pandemic on the predicted performance of regression models. This study proposes the development of previous research following the impact of the COVID-19 pandemic on machine learning performances in predicting economic sectors in Indonesia. The economic sectors mentioned include the exchange rate, CPI, and stock price. The proposed methods for comparison are decision tree (DST) and random forest (RF). Comparison of prediction performance with legacy uses root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Test results show that the RF regression model has superior performance compared to DST with the best MSE, RMSE, MAPE, MAE, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r^{2}$</tex> value of 0.010, 0.102, 0.64%, 0.100, and 0.89, respectively. Using the T-Test, we prove that the COVID-19 pandemic does not significantly affect machine learning predictions on the exchange rate but significantly affects machine learning predictions on CPI and stock prices.