PERBANDINGAN MODEL SIR (SUSCEPTIBLE, INFECTIOUS, RECOVERED), EXPONENTIAL MOVING AVERAGE DAN SINGLE EXPONENTIAL SMOOTHING PADA PERAMALAN COVID-19

Abstract

Novel Coronavirus 2019 (COVID-19) is a disease caused by SARS-CoV-2, COVID-19 is a new type of coronavirus that can be transmitted from human to human. This virus can cause pneumonia, which is inflammation of the lung tissue that causes impaired oxygen exchange, resulting in shortness of breath. Currently it is not known when the Covid-19 pandemic will end, therefore a forecast is needed to predict the spread of Covid-19. This forecasting uses the SIR (Susceptible, Infectious, Recovered), Exponential Moving Average and Single Exponential Smoothing algorithm. Of the three algorithms, which data will be most suitable for forecasting the spread of covid-19 in Indonesia will be compared. The conclusion of the SIR model test results with the PSBB variable inhibits the spread of the virus, the exponential moving average test gets an error value of 24.28% and exponential smoothing gets an error value of 40.07%. So the suitable algorithms used for covid-19 data are the sir model and the exponential moving average.