Bipartite networks composed of dichotomous node sets are ubiquitous in nature and society. Partly for the simplicity's sake, many studies have focused on their projection onto their unipartite versions where one only needs to care about a single type of nodes. When it comes to mesoscale structures such as communities, however, it is ever more important to properly incorporate a priori structural restrictions such as bipartivity. In this paper, as a case study, we take the community structure of bipartite networks in various scales to examine the amount of information of bipartivity encoded in the community detection procedure. In particular, we report the robustness in reliability of detected community based on consistency by comparing the detection algorithm with or without the consideration of bipartivity. From the analysis with model networks embedding prescribed communities and real networks, we find that the community detection tailored to take the bipartivity into account clearly yields more robust community structures than the one without utilizing such structural information. Therefore, it demonstrates the necessity for customizing the community detection algorithm encoding whatever information known about networks of interest, and at the same time it raises an interesting question on the possibility of estimating the quantitative amount of information from such customization.