A Modified Frequency-Based FLRG Fuzzy Time Series Model for National Rice Production Forecasting

Adika Setia Brata, Windy Lestari, Suci Rahmawati, Dimas Jayantara

Abstract


Accurate predictions of national rice production are crucial for food sustainability, yet data fluctuations pose a major challenge. This study aims to improve forecasting accuracy by developing a modified Fuzzy Time Series (FTS) model that simplifies the Fuzzy Logical Relationship Group (FLRG) by retaining only the logical relationships with the highest frequency of occurrence. Monthly Indonesian rice production data from January 2018 to March 2025 were used to test this model. To assess the effectiveness of this modification, the model’s performance was compared with Chen’s conventional FTS models of orders 1 to 3 through MAD, RMSE, and MAPE. Results indicate that the modified third-order FLRG achieved the best accuracy (MAD = 196,410; RMSE = 271,774; MAPE = 5.46%), while reducing FLRG complexity by 10.84%. This demonstrates that FLRG simplification effectively captures longer seasonal dependencies while reducing computational complexity. Nevertheless, the model’s sensitivity to sudden structural changes underscores the need for adaptive or probabilistic FLRG enhancement, with hybrid mechanisms as a potential complement. Overall, the proposed approach provides an efficient decision-support tool for maintaining food supply stability and guiding data-driven agricultural policy in Indonesia. 

Keywords


Forecasting; Time Series; Fuzzy Time Series; FLRG

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DOI: http://dx.doi.org/10.30829/zero.v9i3.26441

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