PROFIT AGENT CLASSIFICATION USING FEATURE SELECTION EIGENVECTOR CENTRALITY

Classification is a method that process related categories used to group data according to it are similarities. High dimensional data used in the classification process sometimes makes a classification process not optimize because there are huge amounts of otherwise meaningless data. in this paper, we try to classify profit agent from PT.XYZ and find the best feature that has a major impact to profit agent. Feature selection is one of the methods that can optimize the dataset for the classification process. in this paper we applied a feature selection based on graph method, graph method identifies the most important nodes that are interrelated with neighbors nodes. Eigenvector centrality is a method that estimates the importance of features to its neighbors, using Eigenvector centrality will ranking central nodes as candidate features that used for classification method and find the best feature for classifying Data Agent. Support Vector Machines (SVM) is a method that will be used whether the approach using Feature Selection with Eigenvalue Centrality will further optimize the accuracy of the classification