<![CDATA[The rapid growth of web technology has led to a significant increase in user interaction data, which can be utilized to better understand user behavior patterns. This study aims to analyze and predict website user behavior using a Deep Learning approach based on Web Usage Mining. The methodology consists of several stages, including user activity log data collection, data preprocessing, pattern extraction, and predictive model development using Deep Learning algorithms. The dataset used in this study includes historical user interaction data such as clicks, session duration, and page navigation paths. The results indicate that the Deep Learning approach is capable of identifying user behavior patterns more accurately compared to conventional methods, while also providing more adaptive predictions in response to dynamic user behavior changes. Furthermore, the developed model can be effectively applied to support recommendation systems and enhance user experience on websites. In conclusion, the integration of Web Usage Mining and Deep Learning proves to be an effective approach for improving the analysis and prediction of website user behavior, contributing significantly to the advancement of artificial intelligence applications in web-based systems.]]>
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