Student Academic Evaluation using Naïve Bayes Classifier Algorithm

One of the department tasks is to predict study duration-time of each student in order to anticipate dropout (DO), which causes the department performance to be poorly. Consequently, study duration-time of each student is indispensable. 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 techniques by using Naïve Bayes Classifier (NBC) has been implemented. The dataset with specific parameters among others 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) of undergraduate program from semester 1–4 with three times graduation criteria (i.e., fast, on, and delay times) have been described and analyzed. The experimental results indicated that accuracy algorithm (AC) of 76.79% with true positive rate (TP) of 44.62% by using quality training data of 80% and 90% have a good performance accuracy value.