Abstract
One point to be assessed in the accreditation process in an institution is the length of the student's study period. The Informatics department in XYZ college has been accredited by the national accreditation bureau for higher education (BAN-PT), but the accreditation has the potential to be improved. One thing that affects the accreditation value is many students did not graduate on time. Therefore, the current study used available student data, both academic and non-academic, using data mining. Two model classifications were used, i.e. Logistic Model Tree (LMT) and Decision Tree J48. The study was aimed to compare LMT and Decision Tree J48 algorithm in predicting the length of student’s study and to find out the influence factors. The data were Informatics Engineering students who have graduated in February 2018 to February 2019 (135 records). Results showed that the LMT algorithm produced an accuracy rate of 71% better than Decision Tree J48 (62.8% accuracy) in predicting the length of the student’s study. The factors influencing the length of study of students are temporary grade point average (GPA) of the first semester, temporary GPA of the second semester, organizational status, and employment status.
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