Prediction of positive Covid-19 confirmation cases in Indonesia with parametric and nonparametric approaches

Abstract

Covid-19 is an infectious disease caused by a coronavirus that was only known when the outbreak began in Wuhan, China in December 2019. A l l countries are infected with this outbreak so that it could impact the S D G program's goal of 2030 not being achieved, namely the target of Good Health and Wellbeing. In addition, a pandemic also affects all sectors of human life, including the national economy and health sectors. Economic growth in Indonesia has decreased by -5.32% in the second quartile of 2020. Meanwhile, in the health sector, some Covid-19 patients can only isolate independently, due to the limited number of health facilities. Therefore, it is necessary to do modeling, where the results of the analysis carried out can help to act appropriately and minimize the impact of the Covid-19 pandemic and ensure a healthy life to meet the welfare of the whole community following SDGs goals. This study uses parametric and nonparametric approaches to predict positive cases of Covid-19 in Indonesia. Modeling using a parametric approach, the A R I M A method is not suitable because it does not meet the assumptions. So that the nonparametric approach to the Fourier series estimator can be an alternative in overcoming this problem. By using a nonparametric approach, the M S E value is 12,472.11 and the determination coefficient is 98.12671%. Based on the results of the test data prediction to be compared with the actual value, the M A P E value obtained is 17.64% and this value is included in the good prediction category. © 2022 American Institute of Physics Inc.. All rights reserved.