Optimizing the best student selection: hybrid K-Means approach and entropy-grey relational analysis - Sulistiani, Heni; Setiawansyah, Setiawansyah; Palupiningsih, Pritasari; Ferico Octaviansyah Pasaribu, Ahmad; Andika, Rio; Hamdan Sobirin, Muhammad (Bulletin of Electrical Engineering and Informatics, 2025)
The selection of the best students is an important process in recognizing students' achievements and dedication in various fields. Through careful and fair selection, students who stand out in both academic and non-academic terms can be identified and assigned. The purpose of the research on the use of hybrid entropy-grey relational analysis (GRA) and K-Means clustering in the selection of the best students is to develop a more objective, accurate, and comprehensive assessment system. The silhouette score results show that 2 clusters have a value of 0.5733, so in this study 2 clusters are used with the best cluster at cluster 0. Data from cluster 0 will be used in determining the best students using hybrid entropy-GRA. The results of the best student ranking using the hybrid entropy-GRA method, for the first best student with a final score of 0.25 were obtained by Mareta Amelia. The hybrid approach of K-Means and entropy-GRA offers a powerful tool to improve decision-making in the student selection process. The hybrid approach of K-Means grouping and entropy-GRA presents a powerful solution, improving the decision-making process and ensuring that high-achieving students are accurately recognized and rewarded.