Fuzzy Time Series Chen Model for Dual-Commodity Agricultural Forecasting: Evidence from Indonesia’s Rice and Corn Production

Authors

  • I Kadek Artha Wiguna )Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • I Gede Iwan Sudipa Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Ni Putu Suci Meinarni Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Ketut Jaya Atmaja Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Anak Agung Gede Ekayana Fakultas Teknologi dan Informatika, Program Studi Rekayasa Sistem Komputer, Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia

DOI:

10.33395/sinkron.v10i1.15584

Keywords:

Fuzzy Time Series Chen, Agricultural Production Forecasting, Food Security Planning, Time Series Modeling, Rice and Corn Prediction

Abstract

Indonesia's strategic food commodities, particularly rice and corn, exhibit strong seasonal fluctuations and irregular production shocks driven by climate anomalies and policy changes, generating nonlinear time-series patterns that conventional statistical models often fail to capture. This study evaluates the forecasting capability of the standard Chen Fuzzy Time Series (FTS) model for dual-commodity agricultural data under varying seasonal and anomaly conditions. Monthly production data from January 2021 to March 2025 from the Indonesian Central Bureau of Statistics (BPS) were processed through a complete FTS pipeline: universe-of-discourse construction, triangular membership function design, fuzzification, FLR and FLRG formation, and midpoint-based defuzzification. Forecast accuracy was assessed using MAE, MSE, RMSE, MAPE, and R², with residual distribution analysis, Shapiro-Wilk tests, and scatter plots conducted to validate model stability. The model achieved high precision with overall MAPE of 4.37% for rice and 8.12% for corn, both classified as Highly Accurate. Monthly accuracy revealed consistent stability during May-December, while transitional months (January-March) showed greater variability due to extreme anomalies such as the January 2024 production collapse. Residual analysis confirmed near-normal error distribution for rice (p = 0.062) and mild deviation for corn (p = 0.031), while scatter plots demonstrated strong linear relationships (Rice R² = 0.9876; Corn R² = 0.9654). The findings establish Chen's FTS as a transparent and operationally reliable baseline method for national food production forecasting, although its sensitivity to structural breaks highlights the need for future hybridization with climate and policy indicators.

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Wiguna, I. K. A., Sudipa, I. G. I., Meinarni, N. P. S. ., Atmaja, K. J. ., & Ekayana, A. A. G. . (2026). Fuzzy Time Series Chen Model for Dual-Commodity Agricultural Forecasting: Evidence from Indonesia’s Rice and Corn Production. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(1), 468-480. https://doi.org/10.33395/sinkron.v10i1.15584

Issue

Vol. 10 No. 1 (2026): Article Research January 2026

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Copyright (c) 2026 I Kadek Artha Wiguna, I Gede Iwan Sudipa, Ni Putu Suci Meinarni, Ketut Jaya Atmaja, Anak Agung Gede Ekayana

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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