The Implementation of the K-Means Clustering Algorithm for Awarding Scholarships to Outstanding Students

Abstract

Offering scholarships is an important tactic to promote higher education and scholastic success among students. The purpose of this study is to improve the impartiality and efficiency of scholarship awarding decision making by developing a decision support system built based on the K-Means clustering method. Students were grouped based on relevant variables and academic achievement requirements using the K-Means clustering algorithm. This approach creates homogeneous groupings based on scholarship recipients' history and academic performance data. The result of this clustering helps in recognizing trends and attributes that form the basis for future scholarship grant choices. This strategy was put into practice by creating a decision support system linked to student information and academic tracking. On processing the clustering data, cluster 0 with the status of eligible scholarship recipients amounted to 43 data and the cluster contained 157 data. It seems to contain data. Cluster 1 includes the status of scholarship recipients who do not meet the requirements. From the results of data analysis, it can be concluded that the scholarship recipient students are really outstanding students.