<![CDATA[Software testing is a crucial stage in the system development lifecycle. Previous studies on SiUKT have used only the black-box method, focusing on functionality without providing insights into performance optimization. This study aims to analyze and improve the performance of the SiUKT API using an intelligent load-testing approach with Apache JMeter. The testing measures three key indicators—response time, throughput, and error rate—across 10 API endpoints with concurrent user simulations of 10, 100, 250, and 500 users. The results show that the SiUKT website performs effectively under moderate load conditions, with an average response time of 338 ms and a throughput of 8.2 requests per second for 10 users. Under high load (500 users), performance declines, with response times ranging from 6 to 8 seconds, while throughput remains stable and the error rate stays at 0.00%. Only the register endpoint experienced a 100% error rate due to validation conflicts. These findings demonstrate the system's ability to maintain stability under varying loads and highlight performance degradation patterns as user traffic increases. The research contributes to the optimization of intelligent system performance by establishing quantitative benchmarks for API scalability and providing recommendations for adaptive infrastructure improvements to support automated intelligent load management.]]>
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