<![CDATA[The purpose of this research is to evaluate and analyze various recommendation system methods that have been applied in the tourism industry, particularly in the city of Palembang. As many as 35 articles published between 2020 and 2025 were thoroughly reviewed using the systematic literature review (SLR) method. The research focuses on three main aspects, namely: the recommendation system approach methods used, the types of datasets utilized, and the evaluation metrics applied in tourism recommendation systems. The research results show that the Hybrid Filtering method, which combines Collaborative Filtering and Content-Based Filtering, consistently delivers the best performance in improving the accuracy and relevance of recommendations, and is effective in addressing the issues of cold-start and sparsity. The most commonly used datasets come from platforms such as TripAdvisor, Yelp, Foursquare, and OpenStreetMap, which provide user review data, geographical locations, and tourist activities. The evaluation of system performance heavily utilizes metrics such as Precision, Recall, F1-Score, MAE, and RMSE.]]>
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