Analysis of New Student Selection using Clustering Algorithms

This research describes the analysis and implementation of clustering method which will be used to process data Seleksi Nasional Masuk Perguruan Tinggi Negeri (SNMPTN) a new student selection, at Surabaya State University. Processing a large number of new student data becomes an annual issue in Surabaya State University. Based on data in 2016, the number of applicants reached 29,779 people. With a large amount of data takes a long time in processing the data to determine the participants who are selected. Our approach uses a clustering method to process participant data and determine the applicant who selected as a new student at Surabaya state university. For analysis and evaluation the accurate and appropriate clustering methods, we selected different clustering techniques that were previously used as benchmarks. The use of clustering may also reduce the cost spent on the application processing and the time the applicants have to wait for the outcome, and could further increase the chances of high-quality applicants getting admission to courses for which they chose. These result also expected can be applied to solve the problem with a similar case.