Profil Kesiapan AI Guru SD di Kabupaten Magelang: Analisis Gaussian Mixture Model dan Efektivitas Pelatihan - Fatikha Akfini Anantaputri; Diyah Ayu Karunianingsih; Yolanda Presiana Desi; Ardian Setio Utomo (MAN 4 Kota Pekanbaru, 2026)
This research aims to map teacher readiness profiles in integrating Artificial Intelligence (AI) and evaluate the impact of formal training on these profiles. Using the Gaussian Mixture Model (GMM) as a soft clustering approach to identify natural and flexible groupings of teacher characteristics, data were analyzed from a convenience sample of 67 primary school teachers in Magelang Regency. Three distinct profiles were identified: Low Readiness, Moderate Enthusiast, and High-Adaptive Readiness. The results show that the majority of teachers (80.6%) belong to the High-Adaptive profile, characterized by high literacy and positive attitudes. Furthermore, Chi-Square analysis and Cramer’s V (V=0.1120) revealed no significant relationship between training history and readiness profiles (p=0.6567). This suggests that current formal training has a marginal practical effect. Policy recommendations emphasize the need for differentiated training and structural support to enhance AI integration effectively.