<![CDATA[The escalating volume and often irregular structure of social assistance data pose significant challenges for efficient data retrieval in management systems. Traditional search algorithms, such as linear and binary search, frequently encounter limitations when handling these large-scale datasets. This research conducts a comparative study between two hybrid algorithms, Jump Binary Search (JBS) and Interpolation Extrapolation Search (IES), aiming to identify the most effective method for a web-based social assistance data management system. Evaluations were performed on a dataset comprising 480 names of social assistance recipients, measuring the number of iterations, execution time, and search accuracy. The results demonstrate IES's superiority over JBS in both iteration efficiency and execution speed. IES exhibited an execution time ranging from 0.002 to 0.006 ms, whereas JBS had an execution time ranging from 0.015 to 0.039 ms. Based on these findings, IES was successfully implemented into a Laravel-based application utilizing a MySQL database. This system is capable of executing searches in less than one second per request. This implementation significantly enhances the system's adaptability and provides an effective search solution for dynamic, large-scale data environments, offering rapid and efficient access to data.]]>
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