Decision Tree Usage for Student Graduation Classification: A Comparative Case Study in Faculty of Computer Science Brawijaya University
Ahmad Afif Supianto, Alfi Julisar Dwitama, Muhammad Hafis · 2018
Decision tree is one of the most prominent method of classification, yet various recent studies focus only on a single type of classification algorithm, thus a comparative study is then proposed to analyze the accuracy of multiple decision tree classification algorithms. This research focuses on the usage of Random Tree, REPTree, and C4.5 decision tree classification algorithm and aims to compares the accuracy of each algorithm. By using the data from Faculty of Computer Science in Brawijaya University as a case study, this research aims to evaluate each algorithm from the three aforementioned algorithms. Furthermore, this research investigates whether the Entry Method and the Gender attributes have a significant influence on decision making in the data classification process. The result shows a higher average accuracy by using C4.5 algorithm with an accuracy rate of 77.01% compared to Random Tree (74.70%) and REPTree (76.75%). The Entry Method and Gender attributes have been proven to negatively influence the accuracy, thus omittance of these attributes is suggested.