Determining student's single tuition fee category using correlation based feature selection and support vector machine

The government has issued the regulation about the enactment of a single tuition fee based on the socio-economic conditions of each student since 2013. All public universities are required to implement this policy. Therefore, each university needs to create a formulation that can be used to categorize a student into which cost group. The results of the data collection found that the parameters used to determine the classification of tuition fees between one universities with another are different. In this research, taken a sampling of student data at one public university database. Before classifying, the attribute of dataset was selected using correlation based feature selection (CFS). The classifier hath has been used in this study is Support Vector Machine (SVM).