Design and Build PMB System with Prediction of Prospective Students Accepted or Withdrawal Using Random Forest Algorithm

Abstract

New Student Admission is one of the essential activities carried out regularly every year or semester. As the operational system of student admissions progresses, student admission data increases yearly. ESA Unggul University (UEU) has not used this data to make strategic decisions, market potential, and consider invitations to enter the academic path. So it is necessary to conduct research whose results can be used by UEU in analyzing prospective students at the time of new student admissions. In this study, data analysis was carried out from 2014 to 2019. This study aims to produce a design using the classification method to predict whether prospective students are accepted or withdrawn. In this study, 19,603 training data and 4,901 test data were used. The results showed the best Random Forest algorithm with an accuracy of 73.61%. The results of this study can be used to support the marketing department in minimizing the number of prospective students who resign.