Pengklasifikasian Metode Support Vector Machine dan Random Forest (Kasus Perusahaan Kebun Kelapa Sawit)

Abstract

Palm oil is one of the leading commodities that support the economy in Indonesia. One of the companies engaged in the oil palm plantation sector has 146 units of oil palm plantations. It is very important to optimize oil palm production, so it is necessary to classify the status of plantation units. Classification aims to predict new plantation units and find the most important variables in the modeling process. The variables used were the status of the garden as a response variable and nine explanatory variables, namely harvested area, rainfall, percentage of normal fruit, fresh fruit bunches production, oil palm loose fruits, production, harvest job performance, harvesting rotation, and farmers. The classification process is carried out using the Support Vector Machine and Random Forest methods to find which method is the best. The data is divided into 80% training data and 20% test data with ten iterations so that ten models are produced for each method. Comparing accuracy value, F1 score, and Area Under Curve (AUC) to evaluate the model. The modeling results show that the random forest method has better performance than the SVM method. The random forest has an average occuracy, F1 score, and AUC, respectively, 90%, 86%, and 89%. Variables of harvest job performance, oil palm loose fruits, harvested area, rainfall, and harvesting rotation are important variables that contribute more than 10% of the model. The results of the research are used for the evaluation and development process of oil palm companies by taking into account the result of important variables that affect productivity and predictive results of new plantation units.