<![CDATA[The rapid development of information technology has driven a massive increase in data volumes in various fields such as tourism, e-commerce, education, and entertainment. To process this big data, data mining is used as a tool to extract valuable information. One of the main applications of data mining is in recommendation systems, which aim to provide suggestions or choices based on previous user preferences or behavior. This study is a systematic review of 15 scientific literature published between 2020 and 2024, focusing on the application of data mining techniques in recommendation systems and analyzing the effectiveness and relevance of the methods used. The study results reveal various widely used algorithms, such as K-Nearest Neighbor (KNN), Decision Tree, Upper Confidence Bound (UCB), ECLAT, and hybrid methods such as Collaborative Filtering and Content-Based Filtering. The performance of each method varies depending on the data type, application context, and evaluation metrics. This study also identifies future trends such as the use of metalearning, AutoML, context-aware systems, and integration with deep learning. It is hoped that this study will be a guide for developers and researchers in choosing the right data mining strategy to create a recommendation system that is accurate, efficient, and able to adapt to changing user needs.]]>
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