Correspondence Analysis (CA) is a statistical technique used to map relationships between qualitative variables. It visualizes data in a low-dimensional space, enabling the interpretation of complex relationships. This study addresses the challenge of visualizing contingency tables with more than three categories using Singular Value Decomposition (SVD) for dimensionality reduction. We apply this approach to stunting data collected by the Indonesian Population Coalition in 2023, focusing on variables such as the district of residence, fever management methods, educational level of caregivers, and sources of information on stunting. The analysis reveals significant associations among these variables, providing insights that could inform public health strategies. This work underscores the utility of CA and SVD in handling high-dimensional qualitative data, particularly in health-related studies.