CLASSIFICATION ACADEMIC DATA USING MACHINE LEARNING FOR DECISION MAKING PROCESS

One of the qualities of higher education is determined by the success rate of student learning. Assessment of student success rates is based on students' graduation on time. The university always evaluates the performance of its students to find out information related to the factors that cause students to become inactive so that they are more likely to drop out and what data affects students ability to graduate on time. The evaluation results are stored in an academic database so that the data can later be used as supporting data when the university makes decisions. The data was processed using the Decision Tree C4.5 method so as to produce a model in the form of a tree and rules. The data used in this study is the graduation data of Informatics Engineering students from 2011 to 2015, totaling 632 data records. Variables used are Nim, in-progress grades each semester, credit taken every semester, GPA, and graduation status. Tests were conducted using split data scenarios with comparison of training data: 90:10, 80:20, 70:30, 60:40, and 50:50. Based on the test results, it is known that the attribute that influences the success of student studies is the grade point average (GPA), where the accuracy of the maximum recognition rate is 88.19% is in the comparison of training data and test data (80%: 20%)