Penerapan Algoritma Fuzzy Time Series Chen untuk Memprediksi Harga Komoditas Kebutuhan Pokok

Rini Widia Putri Z, Purni Munah Hartuti, Roni Al Maududi

Abstract


One of the applied research to predict future data is by using Fuzzy Time Series. This method works based on historical data that is converted into linguistic values. The purpose of this study is to apply the Chen Fuzzy Time Series algorithm in predicting prices and determining the predicted results of the prices of basic necessities (rice, cooking oil, and granulated sugar). The data used in this study are the price data of basic necessities from January 2000 to July 2025. The historical data of each basic necessities commodity is entered into the Chen Fuzzy Times Series Algorithm starting with determining the universe of discourse, forming fuzzy set functions, fuzzification, determining Fuzzy Logic Relational (FRL) and forming Fuzzy Logic Relations Group (FRLG), defuzzification, determining the results and predictions. The results of the study are in the form of monthly forecasted prices that can be directly compared with real prices, as well as the forecasted prices of commodities in August 2025. The forecasting of each commodity shows that the results obtained are in the feasible category or <10% based on the calculation of the Mean Absolute Percentage Error (MAPE).

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