K-Means-Based Behavioral Segmentation of Social Media Users for Digital Communication Analysis in Indonesia - Nurhidayat, Nurhidayat; Nur Fadillah; Dilla, Salsa; Syahrul, Syahrul; Surianto, Dewi Fatmarani (International Journal of Electronics and Communications Systems, 2025)
Social media has become a central part of daily life in Indonesia, yet the rapid growth of digital platforms presents challenges in analyzing large, unstructured user data. Responding to this issue, this study aims to cluster the behavioral patterns of Indonesian social media users using the K-Means Clustering algorithm, a data mining technique for unsupervised segmentation. Employing a quantitative approach, data were collected through an online questionnaire from 553 respondents aged 15–80 years. After data cleaning, normalization, and feature encoding, the optimal number of clusters was determined using the Elbow method and Silhouette Coefficient, resulting in two clusters with a Silhouette score of 0.177. Cluster 0 (303 respondents) represents highly interactive multi-platform users active on Instagram, TikTok, and YouTube for 3–6 hours daily, showing strong interest in entertainment and motivational content. Cluster 1 (250 respondents) includes more passive users, mainly on Instagram and TikTok, spending 3–4 hours per day with moderate engagement and a preference for motivational and self-development content. The findings demonstrate that K-Means Clustering effectively maps user behavior based on platform use, content preferences, motivations, and interaction patterns. The implications of these findings suggest that digital communication strategies need to be tailored to the characteristics of each user cluster so that the messages and content conveyed are more effective.