@thesis{thesis,
		author={Fernando  Renaldi},
		title ={Prediksi Mahasiswa UKMC Yang Berpotensi Mengundurkan Diri Menggunakan Algoritma C4.5},
		year={2022},
		url={http://eprints.ukmc.ac.id/8752/},
		abstract={ABSTRACT

Almost all universities, both PTN and PTS experienced cases of college students resigning. At Musi Charitas Catholic University, there are already many college students who have resigned, amounting to 250 college students from the 2016-2020 batch which have left UKMC prior to June 18, 2021. Too many college students resigning is very concerning because they cannot continue their education process and achieve their ambitions. Therefore, it is neccesary to build an application that can analyze the potential for college students to resign so that college students who have potential to resign can be determined, guided and given more attention by their academic supervisor. The application produced by this research uses data mining techniques to analyze and explore additional information from college student?s data. The data mining algorithm used in this application is the C4.5 algorithm. The C4.5 algorithm is used to create a decision tree that can be used to predict whether a college student have the potential to resign or not. This application is made using MATLAB 2016a and SQLite database. The test results of this application shows that the decision tree made by this application have accuracy of 90% and RMSE error rate of 36.12%.

Keywords: Data Mining, C4.5 algorithm, Prediction, MATLAB}
		}