Implementation and Analysis of Cloud-Native Approaches to Enhance Scalability and Performance in Dwi Jaya Understel Workshop Information System

This study aims to analyze the differences in scalability and performance between a traditional monolithic system hosted on a Virtual Private Server (VPS) and a cloud-native serverless architecture using AWS services for an automotive workshop information system. An experimental method was employed using a post-test only control group design. Performance testing was conducted with K6 as the stress testing tool under a ramp-up load pattern of up to 60 Virtual Users (VU) to simulate peak traffic conditions, while Grafana was used for real-time monitoring and visualization of system metrics.The results indicate that under peak load scenarios, the cloud-native architecture reduced the average response time by 89.1% (from 6.05 seconds to 657.10 milliseconds) and eliminated the error rate completely (from 0.154% to 0%), compared to the monolithic system. Additionally, the throughput improved by 38.2%, demonstrating better responsiveness and stability. These findings confirm that serverless cloud-native systems offer superior scalability and reliability in handling dynamic and high-demand workloads, making them well-suited for public service platforms such as automotive workshop information systems.