Optimization Of Agricultural Production In South Sumatera Using Multiple Linear Regression Algorithm
DOI:
https://doi.org/10.30983/knowbase.v4i2.8754Keywords:
Agricultural, CRISP-DM, Machine Learning, Multiple Linear Regression, OptimizationAbstract
Rice is one of the agricultural commodities in South Sumatra whose productivity level still fluctuates. In 2000, rice production reached 1,863,643.00 kg, then increased to 3,272,451.00 kg, in 2010, but decreased again in 2020 to 2,696,877.46 kg. This instability is influenced by various factors such as land area, rainfall, pest attacks, and fertilizer use. This study aims to optimize rice production by applying machine learning using multiple linear regression algorithms, and the CRISP-DM method, with the stages being business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Data of 1,000 records obtained from farmers were analyzed using Google Collaboratory, resulting in an intercept of -3836,2639, and coefficients for land area of 5,7336, rainfall of 1,2710, pests of 6,1153, urea of 1,6226, and phonska of 1,2581. To evaluate the accuracy of rice production predictions based on these independent variables, calculations were made on the RMSE value and analysis of the coefficient of determination. The results were that the RMSE value was recorded at 17065084,9641, and the coefficient of determination (R²) was 0,6487, indicating that around 64,87 % of the variability in rice production can be explained by independent variables such as land area, rainfall, pest attacks, use of urea fertilizer, and phonska, while the remaining 35,13 % was influenced by other factors.
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