<![CDATA[The management of archival loan data at the West Java Library and Archives Office has not yet been optimally utilized as a source of strategic information. As a result, the potential relationships between archives that are often borrowed together remain unidentified, meaning archival management is not yet fully data-driven. This study aims to identify association patterns between archives using the apriori algorithm, through manual calculations and the use of RapidMiner software, with a minimum support threshold of 20% and a minimum confidence threshold of 50%. The analysis results show that the rule Photo Archive → Guidance Report has a support of 33.33% and a confidence of 53.33% (manual calculation), or 0.333 and 0.727 (RapidMiner). These figures indicate a strong association, as the rule appears in one-third of the total transactions and has a probability of more than 50%. The findings demonstrate that the Apriori algorithm can reveal hidden patterns in archival loan data that had not been systematically analyzed before. These patterns can be leveraged to support archive arrangement, develop archive loan recommendation systems, and plan more strategic archive digitization. In addition, this study opens opportunities for further application of data mining techniques in archival information systems within other government institutions.]]>
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