The objective of this paper is to propose a framework, called Rough Clustering-based Consensus Community Detection (RC-CCD), to effectively address the challenge of identifying community structures in complex networks from a set of different community partitions. The method uses a consensus approach based on Rough Set Theory (RST) to manage uncertainty and improve the reliability of community detection. The RC-CCD framework is tested on synthetic benchmark networks generated by the Lancichinetti-Fortunato-Radicchi (LFR) method, which simulate varying network scales, node degrees, and community sizes. Key findings demonstrate that RC-CCD outperforms established algorithms like Louvain, Greedy, and LPA in terms of normalized mutual information, showing superior accuracy and adaptability, particularly in networks with higher complexity, both in terms of size and dispersion. These results have significant implications for enhancing community detection in fields such as social and biological network analysis.