<![CDATA[Shallots are a strategic commodity that has significant health benefits, including its ability to prevent cancer. The commodity also plays an important role in the agricultural economy, especially in Indonesia, where high demand in domestic and international markets contributes greatly to farmer’s income. However, fluctuations in shallot production often lead to price instability, which has a negative impact not only on consumers but also on the sustainability of farmers' income. This research aims to develop a forecasting model that can assist in more effective planning of shallot production. To achieve this goal, the study tested and compared two forecasting methods: Single Moving Average (SMA) and Single Exponential Smoothing (SES), which are known for their ease of implementation and accuracy in predicting time series data. Using a dataset of shallot production from Brebes Regency over the period 2020-2023, the study found that Single Exponential Smoothing consistently provided more accurate results than Single Moving Average. SES performance is more responsive to recent changes in production data, which is particularly important given the rapid fluctuations that often occur in the agricultural sector. The findings suggest that the application of the SES method in shallot production forecasting can facilitate more informed decision-making in production management and distribution planning, potentially stabilizing market prices and improving farmers' economic conditions]]>
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