Comparative analysis of classification methods in determining non-active student characteristics in Indonesia Open University

Abstract

Classification is a data mining technique that aims to discover a model from training data that distinguishes records into appropriate classes. Classification methods can be applied in education, to classify non-active students in higher education programs based on their characteristics. This paper presents a comparison of three classification methods: Naïve Bayes, Bagging, and C4.5. The criteria used to evaluate performance of three classifiers are stratified cross-validation, confusion matrix, ROC curve, recall, precision, and F-measure. The data used for this paper are non-active students in Indonesia Open University (IOU) for the period of 2004–2012. The non-active students were divided into three groups: non-active students in the first three years, non-active students in first five years, and non-active students over five years. Results of the study show that the Bagging method provided a higher accuracy than Naïve Bayes and C4.5. The accuracy of bagging classification is 82.99%, while the Naïve Bayes and C4.5 are 80.04% and 82.74%, respectively. The classification tree resulted from the Bagging method has a large number of nodes, so it is quite difficult to use in decision-making. For that, the C4.5 tree is used to classify non-active students in IOU based in their characteristics.

Keywords:

• bagging
• c4.5
• classification
• Naïve Bayes
• Indonesia Open University

Disclosure statement

No potential conflict of interest was reported by the authors.