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

W. Yustanti; Y. Anistyasari; Elly Matul Imah

doi:10.1109/ICACSIS.2017.8355029

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

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W. Yustanti, Y. Anistyasari, Elly Matul Imah

2017 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 Vol. 2018-January Conference paper Cited by 3

Abstract

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). © 2017 IEEE.

Affiliations

Department of Informatics Engineering, Universitas Negeri Surabaya, Surabaya, Indonesia; Mathematics Department, Universitas Negeri Surabaya, Surabaya, Indonesia

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