<![CDATA[The rapid digital transformation in educational institutions demands an efficient student grade data processing system capable of handling workloads responsively. This study aims to analyze and compare the efficiency of sorting algorithms (Merge Sort and Quick Sort) and searching algorithms (Linear Search and Binary Search) on a web-based platform. The research method employed is laboratory experimental, testing algorithm performance across various data volume stratifications, ranging from 50 to 1000 entities, using the V8 JavaScript engine. Research findings indicate that Quick Sort possesses superior speed compared to Merge Sort due to its efficient in-place sorting architecture, which minimizes memory overhead and Garbage Collection activity. Furthermore, a performance anomaly was discovered where the Just-In-Time (JIT) Compiler mechanism optimizes execution on large data volumes through a warm-up phase. In the searching aspect, Binary Search demonstrates superior O(log n) logarithmic stability compared to Linear Search, which risks causing interface freezing on massive data. The implication of this study is the critical importance of implementing data pre-sorting protocols to exploit logarithmic search speeds to ensure academic information system scalability. The integration of appropriate algorithms proves to be a crucial foundation in maintaining web application responsiveness amidst the ever-increasing escalation of educational data.]]>
Copyrights © 2026
