<![CDATA[The development of internet technology has made websites the primary medium for delivering information and digital services, especially in e-commerce platforms. However, the complexity of website navigation often causes users difficulties in finding information or products quickly and efficiently. This study aims to analyze website user access patterns using the FP-Growth algorithm to optimize web navigation structures. The research method consists of several stages, including collecting user access log data, data preprocessing, transforming data into transaction forms, and applying the FP-Growth algorithm to identify frequent itemsets and association rules. The results indicate that the FP-Growth algorithm effectively identifies user access patterns, such as relationships between pages frequently visited together, including search pages, product details, carts, and checkout pages. The obtained confidence values indicate consistent navigation patterns among e-commerce website users. Based on these findings, navigation optimization recommendations were developed, including simplifying access flows, placing strategic features effectively, and improving user experience. Therefore, the implementation of the FP-Growth algorithm can help improve website navigation effectiveness in a more adaptive and efficient manner]]>
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