<![CDATA[This research develops a culinary recommendation system in Lombok by integrating the Latent Dirichlet Allocation (LDA) and Content-Based Filtering (CBF) methods. This integration aims to overcome the limitations of pure CBF, which relies solely on basic restaurant attributes and is less capable of capturing the semantic context of tourist reviews. Data was obtained through web scraping of Google Maps using the Apify.com platform, covering 825 restaurants and 20,114 reviews. The research stages included data collection, text preprocessing, topic modeling using LDA, feature engineering, similarity calculation using cosine similarity, and system evaluation. Evaluation was performed using Precision@K, Recall@K, F1-Score, and Mean Average Precision (MAP). The results show that the hybrid CBF+LDA model provides a significant improvement compared to pure CBF, with Precision@3 of 0.9333, Recall@3 of 0.1312, F1-Score of 0.2300, and MAP of 0.9628. These findings indicate that the integration of LDA topics enriches the semantic representation of reviews, thereby improving the relevance of recommendations. This research contributes to the development of artificial intelligence-based tourism recommendation systems and provides practical implications for promoting local cuisine, enhancing tourist experiences, and utilizing digital reviews as a basis for decision-making in the regional tourism sector. ]]>
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