Implementasi Data Mining untuk Prediksi Mahasiswa Pengambil Mata Kuliah dengan Algoritme Naive Bayes - Indra Kurniawan Syahputra; Fitra Abdurrachman Bachtiar; Satrio Agung Wicaksono (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Faculty of Computer Science of Brawijaya University's academic division has tasks for scheduling and determining courses every semester offered for students. However, the scheduling process has some problems such as, many of classes are offered while the students who are interests in that course are very low or vice verca. Therefore, a system is needed that can predict students will take a course or not. One of the solutions is using data mining classification. Based on student's attributes values, grade points, grade point average, semester credit units, cumulative semester credit units, and the semester is used to classify whether the student will take certain courses. Result of the classification divided into two classes that are ‘Yes' for student who take the class and No class for student who put off the class. Classification process is performed using Naive Bayes Classification (NBC) algorithm. The process used data from the odd semester in 2014 to even semester in 2015 for training and from odd semester in 2016 for testing. Prediction result using two courses as sample, the result of accuracy score for Customer Relationship Management course is 85,88%, while for Wireless Network course is 44,92%. The output of this research is a web-based dashboard that displays a comparison of actual dan predict values of each course in certain year and semester.