Identifying Potential Students as College Customers Using Machine Learning: A Literature Review

Abstract

College customers begin with prospective students who are important asset opportunities. Potential prospective students can be identified from academic score data while in SMA / SMK / MAN, achievement data, data on the number of siblings, and data on parents' income. After being a student, their potential can be identified from GPA scores, non-academic achievement data and length of study in college. When becoming an alumnus, college customers can be identified for their potential from the waiting time for alumni to get a job, place of work, and type of alumni work. The use of CRM (Customer Relationship Management) is expected to be able to recognize the potential of college customers to benefit universities. This article contains a systematic literature review of several research themes that have been carried out related to the utilization of machine learning for CRM using several existing machine learning algorithms, The goal of this article is to find new ideas that are better at implementing CRM in college by using machine-based learning methods. The results found turned out that there were only 5 literatures out of 50 that did machine learning method development for CRM or only 5/50 *100% = 10%.