<p>Community detection in social networks constitutes an important field of study within the complex network analysis. It provides invaluable insights into the structures of complex networks, which are becoming increasingly prevalent in the current context of our interconnected world. Numerous community detection approaches proposed to lean on topological metrics, using centrality measures, clustering coefficient, and modularity. These metrics have been widely employed across diverse approaches. However, the question of how semantic properties as topical measure can enrich our understanding of the community structures remains an open research question. In this paper, we present a novel community detection approach leveraging node semantic properties within networks. In fact, we first employ the fuzzy logic to model the inherent uncertainty associated with the semantic attributes. Subsequently, we apply the k-means clustering algorithm to partition the network into communities based on the fuzzy membership function of different attributes. Our proposed approach initially recognizes the semantic dimensions within the node properties and establishes both the related decision and appreciation matrices. Then, it processes a quantitative transformation through the application of the proposed fuzzy technique. This transformation process yields a utility metric, which is later inputted into the k-means clustering algorithm to reveal the community structures present within the network. The proposed approach has been evaluated through experiments, conducted on various network sizes. The obtained results revealed significant findings, highlighting the effectiveness of the proposed approach.</p>