The aim of this study is to explore the pattern of student's period of study by predicting it based on some variables related to students and other variables associated with the study period. The data in this work was from the Faculty of Science and Technology (FST) undergraduate students starting from 2008-2018 from 8 subjects. Those are Mathematics, Physics, Chemistry, Biology, Statistics, Information System, Biomedical Engineering, and Environmental Engineering. The attributes in this study consist of subject, gender, address, high school status, national exam score, admission method, subject selection order, parents' income, ELPT, and GPA. The dependent variable (study period) is divided as on-time and not on-time. The method used in prediction is the Decision Tree with C4.5 algorithm. The results of this study gives information that address and ELPT are not associated with the study period while the most dominant attribute for the prediction is GPA, followed by gender.
|Number of pages||24|
|Journal||International Journal of Innovation, Creativity and Change|
|Publication status||Published - Aug 2019|
- Decision tree
- Study period
- Timely graduation