Model Prediksi Jumlah Produksi Kelapa Sawit Menggunakan Regresi Linear Berganda di PT.Surya Argolika Reksa

Abstract

Palm oil plantations are one of the strategic sectors in Indonesia’s agribusiness industry. To 
support production efficiency and effectiveness, a predictive model capable of accurately estimating 
production volume based on supporting factors is required. This study aims to develop a prediction model 
for palm oil production using the Multiple Linear Regression algorithm by utilizing variables such as land 
area (Ha), number of trees, and rainfall. The data were obtained from the operational reports of PT. Surya 
Argolika Reksa. The model evaluation was conducted using two data splitting scenarios: 80:20 and 70:30. 
The evaluation results show that for the 80:20 test data, the MAE value was 30,095.68, the MSE was 
1,533,325,063.46, and the RMSE was 39,151.33. Meanwhile, for the 70:30 test data, the MAE value was 
35,455.01, the MSE was 2,096,902,404.44, and the RMSE was 45,791.95. These values indicate the level of 
prediction error of the model in units of palm oil production. This research contributes to supporting more 
accurate production planning in the palm oil plantation sector based on data analysis.