COVID-19 Patient Mortality Risk Classification Using Linear Regression and Exponential Smoothing Methods

Abstract

The dominant explanation for this phenomenon is there is no action to prevent a spike in coronavirus. Seeing this problem, we do some literature and research on artificial intelligence and machine learning to support the development of coronavirus forecasting in Indonesia. This research focuses on analyzing future coronavirus case possibilities using machine learning methodology. We use the official Indonesia Covid website data to collect data from the provinces with the largest cases in Indonesia from January until March. In this research, we used supervised machine learning methods of Linear Regression (LR) and Exponential Smoothing (ES) to make it easier for people to understand the data structure adapted to a particular model. The result of our research showed that each province in Indonesia is expected to decrease in new confirmed recovered cases and death cases from April to May while using the Exponential Smoothing method. In the other case, the Linear Regression method showed that cases would decrease in almost all cases in each province, and some cases will increase, such as Recovered Cases and Death Cases in West Java.