Abstrak

Student data and student graduation data at Budi Luhur University produce abundant data in the form of student profile data and academic data. This happens repeatedly and causes a buildup of student data so that it affects the search for information on that data. This study aims to classify student data at the Budi Luhur University Faculty of Information Technology class of 2011 - 2013 S1 and SI study program levels by utilizing the data mining process using classification techniques. The method used is CRISP- DM through a process of business understanding, data understanding, data preparation, modeling, evaluation and deployment. The algorithm used in this study is the Naïve Bayes algorithm. Naïve Bayes is a simple probabilistic based prediction technique based on the application of the Bayes theorem or rules with a strong assumption of independence on features, meaning that a feature in a data is not related to the presence or absence of other features in the same data. The attributes used are Force, Gender, Study Program, Working Status, Scholarship Status, Father's Income, Semester 1 IP, Semester 2 IP, Semester 3 IPS, Semester 1 SKS, Semester 2 SKS, Semester 3 SKS and Judicial Date. The results of this study have an accuracy level of 90.36 and can be used as a basis for making decisions to determine policies by the University of Budi Luhur.

Detail Dokumen