Implementation of Cheng’s Fuzzy Time Series Method for Rice Price Forecasting - Rosni Rosni; Fauziah Afifathuzahwa; Karina Sylfia Dewi; Putri Isnaini Cahyaning Baiti; Nanda Azzanina (bit-Tech, 2026)
Indonesia is an agricultural country where rice is the primary staple and plays a crucial role in maintaining economic stability. However, rice price fluctuations, driven by internal and external factors, often create uncertainty for both producers and consumers. Therefore, accurate forecasting of rice prices is essential to support effective food price monitoring and policy planning. This study aims to forecast rice prices in Bandung City using Cheng’s Fuzzy Time Series (FTS) method. The novelty of this study lies in applying the Cheng FTS approach to analyze recent monthly rice price data and evaluate its forecasting performance in capturing short-term price fluctuations. The dataset consists of monthly average rice prices in Bandung City from January 2022 to June 2025, obtained from the Consumer Price Survey (SHK) published by the Badan Pusat Statistik (BPS). The modeling process involves data preprocessing, interval determination, fuzzification, construction of fuzzy logical relationships, and defuzzification to generate forecasting values. Forecasting performance is evaluated using the Mean Absolute Percentage Error (MAPE). The experimental results show that the Cheng FTS model achieved an MAPE value of 1.54%, indicating very high forecasting accuracy. The predicted rice prices closely track actual price movements, with the average forecast for the next period at Rp15,719. These findings demonstrate that the Cheng Fuzzy Time Series method delivers reliable forecasting performance and can serve as an alternative approach for predicting rice price movements. Furthermore, the proposed model may provide policymakers and related stakeholders with useful insights to support rice price monitoring and stabilization strategies in Bandung City.