One indicator of success in Higher Education is the high dropout rate, of course this condition will make a loss for both students and campus. The problem is the level of Drop out Students every year, especially in Indonesia tend to be unbalanced, that means the number of students who enter greater than students who come out. One of the reasons why the drop out rate in Campus XYZ is because the Management System has not been so well run, such as Lecturer Professionalism, Campus Facility, academic value of the student and the like. This study aims to analyze from several indicators that can be caused by Drop out by sorting the indicator by Analytical Hierarchy Process method. Where with this method can be found the sequence of problems so that the policy taken by the campus in the search for a solution. Also used is the Data Mining process by using Decision Tree C45 which aims to classify the existing dataset. This research contributes to the campus management in sorting indicators of the occurrence of Drop out students and the process of classification of each student, so it is expected to be one solution to solve the problem. The accuracy level resulting from C4.5 is about 97.67% with AUC of about 0.970% categorized as superior classification and AHP with a weighted value of 0.0866 for external effectindicator.