Marketing in travel companies will usually offer promos or recommendations regarding various categories of random tourist objects to their customers. The promo or recommendation contains categories of tourist objects that are frequently visited and had good ratings from many customers. However, because companies do not really know and understand the characteristics or interests of each customer, sometimes some promos do not match their interests so that they are not interested in taking the promos that are offered. There are already several papers that discuss tourism recommendations, but they only focus on 1 tourist spot or tourism object category. Based on these problems, this thesis is made to discuss the segmentation of tourist interest in tourism object categories by comparing the PSO K-Means method and the DBSCAN method, which is about recommendations for more specific tour packages according to rating. Characteristics or similar interests between 1 tourist and other tourists will be grouped into 1 cluster. From each cluster that is formed, it can make it easier for companies to know what categories of tourist objects each customer is interested in or like and be able to offer promos or recommendations for tour packages according to tourist interests.
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