<![CDATA[The availability of sugarcane as the primary raw material for the sugar industry plays a vital role in supporting national food security. Variations in cultivated land area and sugarcane productivity are considered important factors affecting annual sugarcane production. This study aims to investigate the influence of land area and productivity on sugarcane production in Indonesia and to evaluate the performance of Multiple Linear Regression and Double Exponential Smoothing (DES) for forecasting purposes. The study employs secondary time-series data covering the period 1981-2024 obtained from the Central Bureau of Statistics (BPS). Multiple Linear Regression was applied to analyze the relationship among variables, while DES was utilized to forecast future production. Forecasting accuracy was assessed using Mean Absolute Percentage Error (MAPE). The findings indicate that both land area and productivity significantly affect sugarcane production, with productivity identified as the most influential factor. The coefficient of determination (R²) of 0.825 demonstrates that the model explains a substantial proportion of production variability. In addition, DES produced a lower forecasting error than Multiple Linear Regression, with MAPE values of 1.06% and 7.38%, respectively. These results suggest that DES is a more reliable approach for forecasting future sugarcane production. Keywords: sugarcane production, land area, productivity, multiple linear regression, double exponential smoothing.]]>
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