Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation

Abstract

Student academic evaluation is part of academic information system (AIS) performance, in order to control student learning progress is necessary. Furthermore, the evaluation showing whether the student will pass or fail would benefit the student/instructor and act as a guide for future recommendations/evaluations on performance. An in depth study on the student academic evaluation technique by using Decision Tree C4.5 has been conducted. Specific parameters including age, place of birth, gender, high school status (public or private), department in high school, organization activeness, age at the start of high school level, and progress GPA (pGPA) and Total GPA (tGPA) from semester 1–4 with three times graduation criteria (i.e., fast, on, and delay) times have been defined and tested. The scope of the paper has been set for undergraduate programs. The experimental results show that accuracy algorithm (AC) of 78.57% with true positive rate (TP) of 76.72% by using quality training data of 90% have best performance accuracy value.