K-Mean Clustering Algorithm in Grouping Prospective Scholarship Recipients

Abstract

The accumulation files of scholarship recipient candidates currently become a problem in the selection process. Essentially, the accumulated data can be process to produced useful knowledge by utilizing data mining techniques. One of the data mining techniques is clustering that used as a method in this research. The purpose of this research is to generate new knowledge from accumulated data that can be used, one of example is in selecting prospective scholarship recipients. The research method used is the ADDIE version of Research and Development (RnD) (Analyze, Design, Develop, Implement, Evaluate). In generating new knowledge from a data warehouse, the K-Means Clustering Algorithm is used as a method of partitioning data into one or more clusters. The results indicates that the products produced were valid, practical, and effective to be used as a system in grouping prospective scholarship recipient data with the respective values of validity, practicality, and effectiveness were 0.94, 0.91, and 0.93.