Aplikasi Data Mining Menggunakan Algoritme C4.5 untuk Memprediksi Ketepatan Lulus Mahasiswa Berdasarkan Faktor Demografi - Diva Devina; Ahmad Afif Supianto; Welly Purnomo (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Students who graduate not on time are a problem that is still often found in the college academic environment. This was also found in the UB Information Systems study program, wherein 2015-2018 there were 241 students accepted each year on average, while the average student graduated around 130 students. Based on the information, students who graduated and students received were not balanced. So that it can be said that there are still many students who are active and have completed their study period of more than 8 semesters or graduated not on time, this can be detrimental to both students and study programs. Therefore we need a step to help the problem of student graduation accuracy, namely by making predictions using data mining. By utilizing one of the data mining methods namely Decision Tree C4.5, which will later produce a rule in the form of a decision tree. The data used in the data mining process only uses demographic (non-academic) data from students to find out whether the demographics influence the graduation accuracy of students, after that the data is processed using Weka CLI. The results of the algorithm evaluation carried out using the confussion matrix obtained an accuracy rate of 80.4714%. Information about predictions of student graduation accuracy is displayed in the form of a dashboard to make it easier for Kaprodi SI as user. System tested using black-box testing and System Usability Scale (SUS), with the results of valid black-box testing as needed, while the SUS test gets results 67.5.