Penerapan Algoritma C4.5 dalam Klasifikasi Kelulusan Mahasiswa

Abstract

The advancement of information technology and computer science has encouraged various institutions, including higher education institutions, to optimize the use of data in decision-making processes. Each student possesses a unique set of data, which varies depending on their academic and non-academic abilities. The greater the number of students, the more diverse the range of values that can be observed. These data will merely appear as meaningless stacks of information if no in-depth analysis is conducted. However, when analyzed using data mining methods, this data can be transformed into new knowledge that was previously hidden. The objective of this study is to classify student data into two target categories: graduating on time and not graduating on time. The method employed is the C4.5 classification algorithm, using the Weka data mining software. The results of the study show that the data classification achieved an accuracy rate of 91.5663%, and 61 students were predicted to graduate on time out of a total of 83 classified students.