Implementation of the Apriori Algorithm for User Access Pattern Analysis on Debian Web Server

Abstract

The increasing number of website user activities generates large amounts of log data that are often underutilized as a source of information for analyzing user access patterns. This study aims to implement the Apriori algorit hm to analyze user access patterns based on Apache Access Log data collected from a website running on Debian 12 using Apache Web Server and WordPress. The research data were obtained from the access.log file and processed through several preprocessing stages, including filtering, cleaning, and transforming the log data into transaction datasets based on user sessions. The Apriori algorithm was then applied to generate frequent itemsets and association rules using predefined minimum support and confidence values. The results show that the Apriori algorithm successfully identifies relationships among web pages that are frequently accessed together by users. The discovered patterns provide valuable insights into user navigation behavior, enabling website administrators to optimize website structure, improve service quality, and support data-driven decision-making. Therefore, the implementation of the Apriori algorithm on Apache Access Log data can serve as an effective approach for analyzing user behavior based on web server log data.