<![CDATA[Culinary tour is one of the activities that are often done when visiting a city. the mistakes often made because lack of information regarding a location of a culinary tourism can cause a problem for tourists. The purpose of this study is to help the tourists in determining the location of culinary tourism that has the facilities in accordance with what they desired. K-Means Clustering is a method that groups data according to their clusters. Simple Additive Weighting (SAW) is a method for the ranking process by using a preference value. In this study, K-Means Clustering method will divide the location according to the distance calculated from the initial position of the user to the address of the location, then SAW method will sort which location best suit the user's wishes. The testing used 49 location data. The testing process is a accuracy test by comparing result from the system and results from 30 respondents. The results of the testing process is obtained an accuracy of 63.33% for very close category, 40% for near category, and 46,67% for medium category. This system can provide recommendations for very close categories with fairly accurate although for the near and medium category is still not accurate enough.]]>
Copyrights © 2018
