期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：11
DOI：10.14569/IJACSA.2020.0111169
出版社：Science and Information Society (SAI)
摘要：Malaysia citizens are categorized into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. In 2018, it was estimated that 4.1 million households belong to this group. The government of Malaysia has widened access to higher education for the B40 group in an effort to reduce the gaps in socioeconomics and to improve their living standards. Statistical data shows that since 2013, a yearly intake of students in bachelor's degree programs in Malaysia's public universities amounts to more than 85,000. Despite this huge number of enrolments, not all were able to graduate, including students from low-income family background. Data mining approach with machine learning techniques has been widely used effectively and accurately to predict students at risk of dropping out in general education. However, machine learning related works on student attrition in Malaysia's higher education is generally lacking. Therefore, in this research, three machine learning models were developed using Decision Tree, Random Forest and Artificial Neural Network algorithm in order to classify attrition among B40 students in bachelor's degree programs in Malaysia's public universities. Comparative performance analysis between the three models indicates that the Random Forest model is the best model in predicting student attrition in this study. Random Forest model outperforms the other two models in terms of accuracy, precision, recall and F-measure with the value of 95.93%, 97.10%, 81.26% and 88.50%, respectively. Nevertheless, there is a statistically significant difference in performance between the Random Forest model and Decision Tree model but no statistically significant difference between Random Forest models and Artificial Neural Network model.
