Algoritma Naive Bayes untuk Klasifikasi Ketepatan Waktu Kelulusan Mahasiswa Politeknik TEDC Bandung

Abstract

Polytechnic TEDC Bandung  is a higher education institution that is committed to increasing efficiency and effectiveness in the education and teaching process by implementing various policies and programs. One of the aims of this is to produce quality student graduates who are useful for society. One of the steps that students need to take to become quality graduates is to graduate on time. However, in its implementation, there are still some students who experience obstacles in achieving this. This is caused by several factors, so efforts are needed to reduce or even overcome this. This research aims to apply the Naive Bayes algorithm to be able to classify student data that is on time and not on time when attending the final assignment session, in order to obtain solutions and efforts that can help the campus to overcome this problem. The test results using the naïve Bayes method without validation produced 218 data that were included in the on-time class and 33 data that were included in the not-on-time class. Meanwhile, the results of testing using the naïve Bayes method using validation produced 216 data that were included in the on-time class and 35 data that were included in the not-on-time class. Test results using the Naïve Bayes method using validation with RapidMiner produced an accuracy level of 92.05%, precision had a value of 92.40%, Recall had a value of 98.52% and F1-Score had a value of 95.71%.