Model Regresi Linier Berganda untuk Peramalan Hasil Panen Kelapa Sawit - Damanik, Ade Yuri F; Armansyah, Armansyah (Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE), 2025)
This study aims to forecast oil palm production using a multiple linear regression model by considering the relationships among several independent variables: rainfall , number of productive trees , harvested area , labor , fertilizer use , and a seasonal variable . Using secondary data from the company’s Daily Work Logs (Lembar Kerja Harian/LKH), the study employs the Ordinary Least Squares (OLS) estimator to obtain the coefficient vector. The results show a coefficient of determination of 65%, indicating that 65% of the variation in production can be explained by the input variables. The findings indicate that rainfall has a negative coefficient (-0.34305) suggesting a non-linear and inconsistent relationship with production levels. Thus, the study concludes that internal plantation factors—such as the number of productive trees play a more dominant role in increasing yield, whereas higher rainfall tends to negatively affect production. Keywords: Palm Oil Production, Linear Regression, Forecasting, Agricultural Yield, Climatic Influence, Plantation Management.