<![CDATA[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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