Average Based-FTS Markov Chain Based on a Modified Frequency Density Partitioning to Predict COVID-19 in Central Java

Abstract

COVID-19 is still a pandemic in Indonesia, and Central Java is no exception. New positive cases of COVID-19 in Central Java are being discovered every day. Therefore, researchers try to predict new positive cases in Central Java. Many forecasting methods are currently developing, one of which is fuzzy time series (FTS). FTS has been also developed until now, one of which is a development of the FTS by combining the Markov chain as a defuzzification process. In FTS there is no definite formula to determine the length of the interval, so the researcher uses an average based to determine the length of the interval in the FTS Markov chain. Next, the researcher repartitioned based on the modified frequency density. The results of this study are that forecasting new positive cases of COVID-19 in Central Java using the average based-FTS Markov chain based on a modified frequency density partitioning method has a good level of accuracy, this can be seen from the MAPE value of the method.