Implementation of C4.5 Decision Tree Algorithm To Classify Potentially Drop out Students At Universitas Advent Indonesia
Keywords:
Decision Tree C4.5, Drop Out, Data MiningAbstract
One of the factors that determine the quality of higher education is the percentage of students' ability to complete their studies on time. At present, the problem of student failure and the factors causing it to be an interesting topic to research. Higher education institutions need to detect the behavior of students who have an "undesirable" status so that the factors causing their failure can be identified. Based on the description above, it is necessary to analyze student data such as Gender, Age, Religion, Residence, Social Studies, Discipline, and Debt, based on student data that is as much as 98 data so that it can be used in data mining processing. Where data mining is used to dig and get information from large amounts of data. One of the data mining methods is data classification. By using the classification method with the concept of the C4.5 Decision Tree Algorithm, it produces an accuracy of 90.00%, the result of precision is 87.50, and the result of the recall is 100%. It is hoped that it can increase the desire of the University or Higher Education Institution to provide good thoughts, views, and new policies to students who have problems in lectures, in other words maximizing students in an effort to increase the percentage of student interest in college.
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