Segmentation of Teachers Using Gower-Based Hierarchical Clustering - Kaloka, Tesdiq Prigel; Yudhistira, Danang Bagus; Mariyam, Mariyam; Azzahra, Hurul A’ini Sekar; Widagdo, Muhammad Rudito (Jurnal Varian, 2026)
The core issue addressed in this study is the reliance of teacher development and welfare policies on aggregated indicators that obscure variations in teachers’ demographic, professional, and economic conditions. This research aims to identify teacher profiles across multiple educational levels. The method used in this research is hierarchical clustering utilizing Gower distance applied to mixed-type survey data collected from 376 teachers across all educational levels. The analysis incorporates demographic, professional, and socioeconomic variables, including age, education, years of service, income, economic class, number of dependents, income satisfaction, and interest in technology. The analysis identifies two distinct teacher clusters. The first cluster is characterized by more experienced teachers with longer service periods, relatively stable financial conditions, and higher income satisfaction, while the second cluster comprises younger teachers with shorter teaching experience, lower income levels, and lower financial satisfaction. These findings highlight substantial heterogeneity among teachers and suggest that teacher development and welfare policies should be formulated in a differentiated manner by considering career stages and economic conditions, thereby enabling more targeted and data-driven policy interventions.