<![CDATA[Red chili is one of the staple complementary food ingredients essential to society. A rise in the price of red chili peppers can have a significant impact on the community's economy. This study develops a method combining Double Exponential Smoothing (DES) with parameter optimization through grid search and a hybrid approach using a Gated Recurrent Unit (GRU) to predict red chili prices. The goal of this approach is to improve prediction accuracy and find an appropriate solution to refine the forecasting model using double exponential smoothing. In this study, the DES method is used to capture short-term trends in historical data, while the GRU is employed to capture long-term and non-linear patterns in the data that cannot be explained by DES alone. With a data split ratio of 80% for training and 20% for testing, the lowest Mean Absolute Percentage Error (MAPE) achieved is 9.51%. This result is significantly better than using DES alone, which only yielded a MAPE of 32.74%. This study also proves to be able to improve accuracy compared to other methods, which have an average error rate of 22.32%. Therefore, this approach becomes the superior choice as a decision-support tool to anticipate extreme price increases.]]>
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