This research evaluates the performance of an equipment borrowing and returning system for students using three automated website performance assessment tools: Google Lighthouse, Google PageSpeed Insights, and WebPageTest. A total of 28 web pages were selected for evaluation and categorized according to their usage characteristics, namely information pages, list pages, and form pages. The assessment employed Core Web Vitals metrics, including Largest Contentful Paint (LCP), which measures the time required to load the largest visible content element; First Contentful Paint (FCP), which represents the time from the initial page request to the first rendered content; and Cumulative Layout Shift (CLS), which reflects the visual stability of the webpage. The collected data were analyzed using descriptive statistical methods.
        The findings indicate that the case study website achieved acceptable performance levels according to the Core Web Vitals criteria. Google Lighthouse and Google PageSpeed Insights reported LCP and FCP values within the “good” threshold (below 1.5 seconds), whereas WebPageTest consistently produced higher metric values across most pages. In particular, the Cumulative Layout Shift (CLS) value for list-type pages exceeded 0.25, reflecting a more stringent testing environment. Statistical analysis using One-way ANOVA revealed that LCP and FCP differed significantly among the three tools (p < .001), while no statistically significant difference was observed for CLS (p > .05).
        These results suggest that the testing mechanisms of each tool significantly influence the reported performance metrics, particularly those related to rendering time. Therefore, employing multiple evaluation tools in combination can enhance the comprehensiveness and reliability of website performance assessment.