The Application of Data Mining by using K Means Clustering Method in Determining New Students’ Admission Promotion Strategy

Abstract

This study aims to determine the promotion strategy on the admission of new students at the university. Universities need appropriate promotion strategies to increase the number of new students enrolled in subsequent years and to fulfill the equal distribution of new students in each region and study programs at the University. Classification of new student data reception at the Indo Global Mandiri University (IGM University) in 2018/2019 uses the CRISP-DM data mining application (the Cross-Industry Standard Process for Data Mining) using the K-Means grouping method. Research data using primary and secondary data. The population and sample of the study were 1011 students using 4 (four) attributes in this study, namely the name of the student, the area of origin, the study program, and the promotion strategy (direct visit, word of mouth, media, brochures, and coming directly). This test is carried out with the Waikato Environment for Knowledge Analysis (WEKA) 3.8 tool. The results of this study indicate that the direct visit strategy is the most appropriate in the admission of new students at IGM University, amounting to 492 students with 26%, with this strategy being able to absorb many new student candidates from various regions including Palembang, Regency / City, and regions in outside South Sumatra, there is also equality in various study programs at IGM University. Word of mouth promotion strategies and media are optimized to be included in the promotion team in determining the promotion strategies in the following year to increase the number of new student admissions.