Prediksi Kelulusan Mahasiswa Berdasarkan Kinerja Akademik Menggunakan Pendekatan Data Mining Pada Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Brawijaya - Ryan Dwi Pambudi; Ahmad Afif Supianto; Nanang Yudi Setiawan (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Late graduation is a problem that often encountered in university's academic environment. This is also experienced in University of Brawijaya Information System study program which average students accepted in the Information Systems study program is 213 students, whereas the average students that graduate is only less than 99 students. This imbalancy will be certainly causing disadvantage to the academicians and students. So based on this problem t is necessary to predict the students who are indicated not to graduate on time so that further action can be given. One of the tasks that can be used to predict graduation in data mining can use the type of task classification. By utilizing one of the classification algorithm methods namely Naive Bayes, probability patterns will be generated on each attribute that can be used to determine whether students graduate on time or not. From data of students that collected totaling 1354 data, the data is then carried out pre-processing for the mining process on a system developed on a web-based using Weka CLI. Information from graduation predictions is displayed on the dashboard according to the needs of the Head of the SI Department. Test Results Black-box shows a valid system according to defined needs. While the results of usability testing with the System Usability Scale(SUS) produce a value of 57.5 which is classified as an Adjective rating Good.