Daily Rice Sales Prediction Based on Brand, Price, and Package Size Using Linear Regression and Random Forest Methods
Prediksi Penjualan Beras Harian Berdasarkan Merek, harga, dan Ukuran Kemasan Menggunakan Metode Linear Regression dan Random Forest
DOI:
https://doi.org/10.21070/wbgg5k34Keywords:
Sales Prediction, Linear Regression, Random Forest Regression, Machine Learning, Rice SalesAbstract
Sales prediction helps identify sales patterns and supports inventory planning using historical data. This study predicts daily rice sales and compares Linear Regression and Random Forest Regression across different attribute counts and data splits. The dataset covers August 2025 to May 2026, with 4,420 initial records and 4,301 records after preprocessing. Eighteen configurations were tested using 7, 5, and 3 attributes with training-to-testing ratios of 80:20, 70:30, and 60:40. Random Forest Regression with 5 attributes and an 80:20 split achieved the lowest errors: MAE 2.3805, MSE 17.6392, and RMSE 4.1999. In the test data, 2 Rajawali had the highest average predicted sales by brand at 7.32 units, while March 2026 had the highest monthly average prediction at 5.72 units. Among groups with at least three test observations, 2 Rajawali rice in 25 kg packages recorded the highest average prediction of 12.57 units in August 2025.
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