<![CDATA[Lampung, one of the provinces in Indonesia, is recognized as one of the main producers of cassava in the country. Cassava plants hold significant value as raw materials in the food industry and other industrial sectors, particularly in tapioca production. To anticipate the raw material needs in industries, production forecasting becomes a key aspect of effective planning. Various forecasting methods are employed, including Linear Regression, Moving Average, Weighted Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend. Forecast evaluations are conducted by measuring Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). Analysis indicates that the Linear Regression method proves to be the most effective in forecasting cassava production in Lampung Province. This method yields the lowest MSE, MAD, and MAPE values compared to other methods, signifying a higher level of accuracy in cassava production forecasting. Thus, the utilization of this method can serve as a strong foundation in more efficient and effective production planning in the cassava sector in Lampung. This provides a crucial basis for making more informed decisions in production and inventory management, enhancing industrial performance, and ensuring smooth raw material supply.]]>
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