Penerapan Algoritma K-Means untuk Pengelompokan Data Mahasiswa Baru Program Studi Teknik Informatika di Universitas Pahlawan Tuanku Tambusai - Kasini, Kasini; Rusnedy, Hidayati; Tanjung, Lailatul Syifa; Munti, Novi Yona Sidratul (Jurnal Teknik Industri Terintegrasi (JUTIN), 2025)
Pahlawan Tuanku Tambusai University (UP) in Riau Province has an Informatics Engineering Study Program that accepts new students every year from various regions around Bangkinang. Incoming student data is processed to assist decision making, especially in the field of promotion. This study aims to apply the K-Means algorithm to Informatics Engineering Study Program student data, with attributes of student name and district of origin, to group regions based on promotion potential. The K-Means method is used to group data into three clusters: High Priority, Medium Priority, and Low Priority. The results of the analysis show that there are 22 regions included in the High Priority Cluster, 23 regions in the Medium Priority Cluster, and 43 regions in the Low Priority Cluster. Regions in the High Priority Cluster are the main priority for promotion strategies, while regions in the Medium Priority and Low Priority Clusters require a more focused promotion approach. This study provides an important contribution to the promotion strategy of the Informatics Engineering Study Program at UP by using a data mining approach to increase the visibility of the study program in the community