<![CDATA[Data search efficiency is a crucial aspect of information system performance, particularly in applications handling dynamic data volumes within the public and private sectors. This study aims to analyze optimization strategies for the Linear Search algorithm through a user access pattern approach and to compare the technical performance between iterative and recursive implementation methods. The research methodology employs a quantitative approach involves computational experiments on datasets ranging from 1 million to 100 million records, alongside qualitative case studies on various existing information systems such as libraries, retail, and civil registry services. Test results indicate that despite sharing the same asymptotic time complexity, the iterative method proves to be far superior to the recursive method, demonstrating a significant execution time difference (0.0133 seconds versus 28.38 seconds on 100 million records) and more efficient memory usage. Furthermore, this study finds that the effectiveness of Linear Search can be drastically improved through the application of access pattern-based heuristic strategies, such as Move-to-Front for popular data and Timestamp Ordering for chronological data. The conclusion of this research recommends the use of the iterative Linear Search method combined with adaptive data position management as the optimal solution for systems characterized by dynamic and unordered data.]]>
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