<![CDATA[Jakarta as the center of the capital city of Indonesia which has culinary attractions that are of interest to visitors from international restaurants to the best traditional Indonesian restaurants. Both are presented with the feel of an elegant colonial building up to a modern nuanced room.Therefore, the K-Means clustering method and Simple Additive Weighting (SAW) are combined to select and classify the data needed in the ranking list of culinary tourism locations in Jakarta that are in accordance with the user's wishes or according to the user's initial location. K-Means Clustering is a method that groups data according to each cluster.Simple Additive Weighting (SAW) is the method used for the ranking process with use preference values. In this study, the K-Means Clustering method will divide the location travel according to distance calculated from the user's initial position to the address of the tourist location, then the SAW method willsort which location best suits the user's wishes. The test results show that the K-Means Clustering and Simple Additive Weighting (SAW) method can buy recommendations on culinary tourism locations with the best ranking from each cluster based on the weight of each criterion and the maximum radius input by the user]]>
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