<![CDATA[This study compares the performance of the SARIMA and Exponential Smoothing (Holt-Winters) models in forecasting the Farmer Exchange Rate (NTP) for Central Java Province from 2016 to 2025. The monthly statistical data used was obtained from the Central Java Provincial Statistics Agency. The models were evaluated using MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) on test data for the period January 2016 to September 2025, while forecasting was carried out from October 2025 to December 2027. The results show that the SARIMA (1,1,1) (1,1,1,12) model has an MAE of 6.94 and an RMSE of 7.88, indicating that the model can make accurate predictions with few errors. However, the Exponential Smoothing model has a lower MAE and RMSE, implying that this model is more accurate in forecasting long-term NTP. Both models show comparable seasonal trends, with Exponential Smoothing being more stable and sensitive to seasonal changes. This study also proposes the use of alternative forecasting approaches, such as ARIMAX, VAR, or machine learning to improve the accuracy of future forecasts. The results of this study can be used to develop agricultural policies that maintain food price stability, improve farmer welfare, and predict future inflation fluctuations.]]>
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