The productivity of oil palm is highly dependent on the application of fertilization. Fertilizer applications must pay attention to its effectiveness and efficiency so that nutrients are absorbed optimally at the right time and right dosage. This research was conducted to assess the effectiveness of fertilization time. In addition, this also aimed to examine the effectiveness of oil palm fertilization which affects the productivity of fresh fruit bunches (FFB) and increases the production income. The data used were secondary data including fertilization and productivity of FFB at time intervals after fertilization for 6, 7, and 8 months in Kujan Estate Division, PT Menthobi Makmur Lestari (MMaL), Kujan Village, Nanga Bulik District, Lamandau, Central Kalimantan Province. The data obtained were analyzed using MS software. Excel was also employed to describe the dosage and time applied for 1 year and the productivity of FFB after 6 months of fertilization. Fertilizer application in months with high rainfall and long dry season is less effective and efficient for nutrient availability. The productivity of FFB in the second semester after 6 months of fertilization has increased compared to the first semester. The effective application of fertilizers can increase the production of CPO and reduce the total maintenance cost of the oil palm company

PT Menthobi Makmur Lestari is a growing industrial company in the field of palm oil production. The company is targeting increased palm oil production to forecast capacity plans and manufacturing facilities. One of the prediction methods used is multiple linear regression. The free variables used for prediction are the age of the tree, land area (ha), number of trees, number of vines, and the bound variable is the yield of oil palm production. The results of the correlation test using multiple regression showed a significant correlation of 0.05 or less. Hypothesis testing includes multiple linear regression and correlation using the t test and f test with a significance level of α = 0.05. The value of multiple correlation analysis (R) is 0.947 and the coefficient of determination is 90%. The performance of the multiple regression equation is the accuracy of predictions with an average absolute error percentage (MAPE) of 21%, which is formed from the validation of training and testing data.