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Renatalia Fika
Akademi Farmasi Dwi Farma Bukittinggi, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Dentistry Economics, Econometrics & Finance Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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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) Vol 5, No 3 (2022): Budapest International Research and Critics Institute August
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i3.6757

Abstract

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%. .
Co-Authors Abdah Muhsanah Adek Septiani Ratna Sari Ahmad Asrawi Ainun Naim Annisa Lufiah Safitri Arfiandi Arfiandi Asih Permani Putri Asima Sihol Marito Marpaung Budi Setiawan Budi Setiawan Budi Setiawan Budi Setiawan Cakranegara, Pandu Adi Dewi Novita Ezy Wirna Ilza Fitri Handayani Lovyola Adma Putri Mazaya Fadhila Mazaya Fadhila Mevy Trisna Mevy Trisna Mevy Trisna Mevy Trisna Muhajri Agusfina Muhammad Ainun Naim Muhammad Najib Mubarrok Neri Fadjria Nofita, Dewi Nurhida Yanti Putri Buana Ningrum Putri Ulandari Refdia Yola Rini Wulandari Uli Wildan Nuryanto Vera Triciana Wenni Yonrizon Yonrizon Yonrizon Yulia Ningsih Zulfisa Zulfisa Zulfisa Zulfisa Zulfisa, Zulfisa