Perbandingan metode double exponential smoothing dan artificial neural network untuk meramalkan perkembangan covid-19 di Indonesia

Abstract

Forecasting is the process of systematically estimating what might happen in the future based on past and present information (historical data) held so that errors can be minimized. Covid-19 is designated as the latest global pandemic by the World Health Organization (WHO) where Indonesia is one of the countries affected by the Covid-19 outbreak. The method used in this research is forecasting method with Double Exponential Smoothing and Artificial Neural Network. This research was conducted to predict the number of positive cases, death and recovery due to Covid-19 that occurred in Indonesia for the next 31 days ie from July 12, 2020 to August 11, 2020. Based on the analysis, it was found that forecasting the number of positive cases, death and recovery due to Covid -19 is more suitable for the Artificial Neural Network method which is based on the smallest RMSE value. This Artificial Neural Network method has a RMSE value that is much smaller than using the Double Exponential Smoothing method, where the RMSE value for positive cases is 95.84, death cases are 1.97, and recovery cases are 69.03