Fuzzy Time Series Markov Chain for Rice Production Forecasting - Muhammad Najib Mubarrok; Uli Wildan Nuryanto; Renatalia Fika; Pandu Adi Cakranegara; Adelin Elsina Tanati (Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 2022)
Forecasting is an activity to get an estimate of the value that will appear in the future by paying attention to past events. Forecasting can be used as decision support in determining a policy in various fields. Forecasting can be done using statistical methods such as regression analysis, trend analysis, MA, and ARIMA. In this paper, the fuzzy time series markov chain method will be forecast which will be applied to rice production data in D.I. Yogyakarta Province. The fuzzy time series markov chain method was chosen because it does not need to meet certain assumptions so that the fuzzy time series markov chain can be applied to time series data with stationary and non-stationary patterns. This study aims to analyze fuzzy time series markov chain for rice production forecasting. This study uses time series analysis. The data used in this research is secondary data. The data used in this study is data on rice production in D.I. Yogyakarta Province in 1970-2017 taken from the website www.pertanian.go.id. Historical data consists of 48 data. Solving this research problem using fuzzy time series markov chain method. The results of the study show that forecasting with 11 fuzzy sets is declared the best forecast with a mean absolute percentage error of 4.156%. .