Analysis of the application of Gemini AI for informatics learning using the k-means clustering algorithm - Bayu Eka Susanto; Sujatmiko Ginting; Andisyah Putra; Jheki Pranta Singarimbun; Ridwan; Sigit Prabowo (Yayasan Sinergi Multidimensi Kreatif, 2025)
This research aims to analyze the learning performance of vocational high school students in Informatics subjects thru the integration of Generative AI (Gemini) with the K-Means Clustering algorithm, an approach that is still rarely applied in the Indonesian educational context. The research data includes 28 students, consisting of 8 males and 20 females, whose performance was analyzed based on assignment, quiz, and exam scores. The clustering results yielded three main performance groups: high, medium, and low clusters. Gemini integration helps accelerate the process of interpreting the clustering results and provides a deeper understanding of students' learning patterns. The research findings indicate that the combination of generative AI and cluster analysis can generate more accurate insights to support the implementation of adaptive learning and data-driven decision-making by teachers. Additionally, this study contributes to the development of learning analytics at the vocational education level, while also opening opportunities to implement more personalized learning strategies. Further research could expand the data scope, test other clustering algorithms, and develop an analytics dashboard to facilitate data utilization by educational institutions.