Penerapan Fuzzy Time Series Markov Chain dalam Peramalan Harga Cabai Merah Berbasis Data Harian di Kabupaten Banyumas - Dian Kartika Sari; Iqsyahiro Kresna A; Diah Septiani (Universitas Negeri Gorontalo, 2026)
Relatively high fluctuations in red chili prices often create challenges in maintaining food commodity price stability in various regions, including Banyumas Regency. Price changes influenced by weather conditions, supply availability, and market demand make red chili prices difficult to predict accurately. Therefore, a forecasting method capable of modeling uncertain and fluctuating time series data is needed. This study aims to forecast red chili prices in Banyumas Regency using the Fuzzy Time Series Markov Chain (FTSMC) model. The study used 317 daily price data points collected from January 1, 2025, to March 10, 2026. The research stages included determining the universe of discourse, constructing intervals and fuzzy sets, fuzzification, forming Fuzzy Logical Relationships (FLR) and Fuzzy Logical Relationship Groups (FLRG), constructing the Markov transition probability matrix, and performing defuzzification to obtain prediction values. The contribution of this study lies in applying the FTSMC method to model regional red chili price fluctuations with volatile characteristics and evaluating its performance using Mean Absolute Percentage Error (MAPE). The results indicate that the Fuzzy Time Series Markov Chain method can effectively model the fluctuation patterns of red chili prices. Based on the evaluation results, the prediction model achieved a MAPE value of 3.19%, indicating very high prediction accuracy. Therefore, the FTSMC method can be used as an effective alternative forecasting model for predicting red chili prices and supporting decision-making related to food commodity price control in Banyumas Regency.