Faktor-Faktor yang Mempengaruhi Hasil Produksi Kelapa Sawit dengan Analisis Regresi Linear Berganda di PT. Perkebunan Nusantara IV Unit Dolok Sinumbah
DOI:
https://doi.org/10.24114/jmk.v6i3.22210Keywords:
Linear regression, Classic assumption, multiple linear regression.Abstract
Regresi linier berganda adalah suatu model yang melibatkan lebih dari satu variabel bebas dalam membuat suatu persamaan regresi linier. Pengaruh dua varibel bebas (X) atau lebih terhadap satu variabel tak bebas (Y). Dalam penelitian ini model tersebut digunakan untuk mengetahui pengaruh luas lahan, pupuk, bibit dari jumlah tanaman belum menghasilkan (TBM), curah hujan, hama, pengendalian gulma dan tenaga kerja terhadap produksi kelapa sawit. Hasil analisis regresi linear berganda menunjukkan bahwa luas lahan, pemakaian pupuk, pengendalian gulma, dan tenaga kerja berpengaruh nyata terhadap produktivitas kelapa sawit. Persamaan regresi diperoleh: Y = 188.923,302 + 1, 375X1 + 4, 013X2 - 0, 887X3 - 2, 718X4 + 5, 923X5 - 14, 810X6 + 1.027, 265X7. Dengan nilai koefisien determinasi sebesar 9,77 persen. Uji asumsi klasik yang dilakukan terhadap model menunjukkan bahwa tidak terdapat multikolinearitas dan heteroskedastisitas, dan data sudah terdistribusi normal, sehingga model layak digunakan.ABSTRACTMultiple linear regression is a model involving more than one independent variable in creating a linear regression equation. Influence of two independent variables (X) or more to one independent variable (Y). In this study the model is used to determine the effect of land area, fertilizer, seeds from the number of unprofable plants (TBM), rainfall, pests, weed control and labor to palm oil production. The result of multiple linear regression analysis showed that the area of land, the use of fertilizer, weed control, and labor significantly affect the productivity of oil palm. Regression equation obtained: Y = 188.923,302 + 1, 375X1 + 4, 013X2 - 0, 887X3 - 2, 718X4 + 5, 923X5 - 14, 810X6 + 1.027, 265X7. With coefficient of determination equal to 9.77 percent . The classical assumption test performed on the model shows that there is no multicollinearity and heteroscedasticity, and the data has been normally distributed, so the model is worth using.References
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