Optimasi Strategi Promosi Sekolah SMK melalui Segmentasi Data Siswa Baru dengan Clustering Metode K-Means menggunakan Differential Evolution (DE) - Pebruarianto Hutabarat; Adil Setiawan; Bill Raj; M Prasetyo; M. Agung Irnanda; Empiter Gea; Johan; Andreas Parapat (LKP KARYA PRIMA KURSUS, 2026)
SMK XYZ faces challenges in developing effective and efficient promotional strategies to attract prospective new students. Previously, promotional approaches have been general and failed to address the specific needs of different prospective student segments. This research aims to optimize school promotional strategies by analyzing patterns in new student characteristics through data segmentation techniques. The proposed method is K-Means Clustering optimized with the Differential Evolution (DE) algorithm. DE optimization addresses K-Means' sensitivity to initial cluster center initialization, aiming for more stable and optimal segmentation. The data used includes demographic attributes, major interests, registration pathways, and prior school origins of new students from the 2023/2024 cohort. Research results show that the DE-K-Means combination produces more compact clusters (lower within-cluster sum of squares values) compared to standard K-Means. Based on the resulting cluster analysis, three distinct promotional strategies are formulated for each prospective student segment: digital-intensive approaches, partnerships with feeder schools, and highlighting specific major advantages. Implementing these strategies is expected to significantly increase the quality and quantity of new student admissions.