<![CDATA[The XYZ University Library faces recurring book losses that affect the efficiency of library collection management. This study aims to develop a data-driven analysis system to identify loss patterns and support decision-making. The proposed solution uses a Data Warehouse approach with the Kimball Four-Step method and the ETL (Extract, Transform, Load) process. This methodology includes business process selection, grain declaration, dimension identification, and fact determination. Library transaction data from 2022 to 2024 was extracted, transformed, and loaded into a MySQL-based warehouse and visualized using Power BI. The analysis revealed that popular book categories, such as novels, were the most frequently lost. The visualization also enabled trend analysis based on time, book types, and user segments. The findings highlight a significant decline in loss cases, from 27 in 2022–2023 to 13 in 2023–2024, suggesting improved monitoring and management. The study demonstrates that the Data Warehouse approach effectively supports historical data analysis and provides accurate insights for sustainable library policy formulation.]]>
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