Data-Driven Insights for Higher Education Marketing: Segmenting Applicant Pools Using K-Means Clustering - Jafar Shadiq; Harjunadi Wicaksono; Rahmat Budiarto; Raisha Nur Salamah; Zidan Al Buqhori Fakhrudin (LPPM Universitas Islam 45 Bekasi, 2025)
This research aims to optimize marketing strategies for new student recruitment at Bina Insani University (BiU), which faces intense competition. The current marketing efforts are generic and inefficient. Utilizing the CRISP-DM framework, this study applies the K-Means clustering data mining method to analyze primary data from applicants from 2021 to 2024. The analysis focuses on the attributes of previous school major, information source, and location. The findings successfully identified four distinct segments of prospective students: the