Implementation of the Decision Tree Method Predicting Student Graduation on Time With C5.0 Algorithm

The percentage of students who graduate on time is one of the benchmarks for assessing a university's accreditation. To get good accreditation results, it is necessary to predict the graduation rate using a method so that students can anticipate delays in graduating. The C5.0 algorithm is a Decision Tree based algorithm which is a refinement of the ID3 and C4.5 algorithms created by Ross Quinlan in 1987. In this research, researchers analyzed several variables used in the input variables, there are several variables used, namely the SKS variable, Academic Leave, Student Status, Relationship/Communication with Family, Interests and Talents, Family Economics, Relationship/Communication with Lecturers, Number of Revisions, Final Assignment Material or Method. Meanwhile, in the target variable there is the Study Period. The aim of this research is to obtain the results of the implementation of graduation predictions for Strata 1 (S1) Mathematics Study Program students, Faculty of Science and Technology, UIN North Sumatra Medan using the C5.0 Decision Tree Algorithm. There are several steps taken, namely data preprocessing, data division, calculating information gain for the entire case, calculating information gain and entropy for each attribute. Calculate the total entropy and Gain value of each attribute, create a branch from each root node category, if the result of the branch becomes an internal node, the process is carried out until the attribute categories cannot be split. After carrying out the analysis, the accuracy rate was 82.81%. With a high level of accuracy, it can be developed into a rule that can provide predictions or input in making decisions about whether students will graduate on time