Community detection is a fundamental task in network analysis, but traditional modularity-based methods often overlook node attributes that provide crucial semantic information. We propose an attribute-aware null model that extends the Louvain algorithm by incorporating node feature similarity into the expected edge weight calculation. Our approach introduces a theoretically grounded modification that preserves modularity properties while capturing both structural and semantic coherence. Unlike existing attribute-aware methods that require deep learning or complex optimization, our method maintains the simplicity and scalability of Louvain while achieving improved performance. Experiments on Cora and Citeseer citation networks demonstrate that our method achieves competitive modularity scores and significantly better alignment with ground-truth communities compared to both classical and recent baselines. The proposed approach offers a practical solution for networks where node attributes provide meaningful community indicators.