Model Prediksi Jumlah Produksi Kelapa Sawit Menggunakan Regresi Linear Berganda di PT.Surya Argolika Reksa - Irpan M irpan; Unang Rio; Karpen; Hamdani (PPPM Institut Teknologi Pagar Alam, 2025)
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.