In relational database systems, normalization plays a major role in reducing duplicate data while ensuring information remains consistent. Yet, databases designned with full normalization often require multiple join processes when running queries, which can slow down performance. On the other hand, denormalization intentionally keeps certain repeated data to speed up access, especially in systems that rely heavily on reading operations. This research exam ines how both database design approaches-normalized in 3NF and denormalized structures-perform under similiar conditions. Two database schemas that share the same logical design but differ in physical implementation were tested using synthetic datasets on a current Database Manajemen System (DBMS). The evaluation focused on execution time, logical data reads, and storage requirements. From this study, practical insights are expected to help determine which database design strategy is more suitable depending on the characteristics of the workload and operational usage.
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