ARTIFICIAL NEURAL NETWORK APPLICATION IN MODELING MORTALITY OF COVID-19 PATIENTS IN INDONESIA

Abstract

. The Indonesian government and public healthcare system have been under massive pressure due to increased infections and mortality rates among Covid-19 patients. An appropriate model is needed to model the mortality of Covid-19 patients in Indonesia to help the Indonesian government develop the right policy for dealing with the Covid-19 pandemic. Artificial neural networks are increasingly popular in various research fields. Artificial neural networks can detect specific patterns in mortality modeling. In this study, we use artificial neural networks to model the mortality rate of Covid-19 patients in Indonesia. We try combinations of activation functions, learning rates, and hidden layers for the best predictions. We compare the prediction accuracy of artificial neural networks with that of the Holt-Winters method. The results showed that the best model of artificial neural networks produced an RMSE of 3.0530. In contrast, the Holt-Winters method produced an RMSE of 664.9022. Therefore, the artificial neural networks performed better than the Holt-Winters method in analyzing mortality data of Covid-19 patients in Indonesia.