Clustering of Student Personality Types Based on Extracurricular Activities using The K-Means Algorithm Approach - Rifdah Syahputri; Ilka Zufria (Journal of Digital Technology and Computer Science, 2026)
Purpose – This study aims to identify patterns of student personality tendencies at Universitas Islam Negeri Sumatera Utara (UINSU) using a data-driven clustering approach based on extracurricular participation characteristics and personality-related questionnaire responses. The study was motivated by the need to systematically analyze the relationships and patterns among students' organizational activity, activity frequency, organizational roles, social interaction, and personality tendency characteristics. Methods – This study employed a quantitative approach using data mining. Data were collected through a questionnaire distributed to UINSU students, resulting in 604 respondents. The data were processed through cleaning, missing-value checking, transformation, and Min-Max normalization. The K-Means Clustering algorithm was applied to group students according to their extracurricular activity patterns. The clustering quality was evaluated using the Silhouette Score, while PCA was used to visualize the clustering results. Findings – The clustering process identified three personality tendency groups: Extrovert, Introvert, and Ambivert. The resulting clustering obtained a Silhouette Score of 0.3823, indicating a moderate level of cohesion and separation among the clusters, although some observations remained relatively close to other clusters. Research implications – The findings provide an overview of student personality tendencies based on extracurricular activity patterns. However, the results represent personality tendencies derived from clustering characteristics and should not be interpreted as psychological diagnoses. Originality – This study applies K-Means Clustering to extracurricular activity data as a basis for identifying student personality tendencies into Introvert, Extrovert, and Ambivert groups, providing a data-driven perspective for understanding student characteristics.