Corn sales analysis using linear regression and svm methods to improve production planning
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Abstract
This research aimed to analyze and predict corn sales at UD Muara Kasih to improve production planning accuracy. The study used historical corn sales data collected over a specific period, covering 42 data entries from January 2021 to December 2024. The dataset included variables such as sales date, quantity sold, selling price per ton, total sales value, weather conditions, market demand (in tons), and the number of laborers. Prior to model training, the data underwent comprehensive preprocessing involving data cleaning, feature extraction, and normalization to ensure its quality and readiness for analysis. Two predictive models were applied: Linear Regression and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Simulation data for 2024 and 2025 were generated based on the monthly averages derived from the historical dataset. The results showed that the Linear Regression model produced more stable predictions with a lower Root Mean Squared Error (RMSE) of 255.84 compared to the SVM model’s RMSE of 256.42. While the SVM model showed greater responsiveness to seasonal variations, the Linear Regression model was identified as the most suitable for the given dataset. The predictive models developed in this study are expected to support UD Muara Kasih in making more accurate and informed production decisions in the future.
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