Machine Learning in the Development of Integrated Tourist Destinations

Cultural diversity, history and natural charm make West Sumatra worthy of being a leading tourist destination. Culinary diversity is the main attraction in the tourism sector in West Sumatra Province. Along with these developments, various tourist destinations have sprung up including shopping tourism destinations, underwater tourism, theme tours, and historical tours, but the grouping of tourist destinations based on variables has not been optimally carried out. This research was conducted to classify various tourist destinations based on the variables in tourism, namely mileage, transportation facilities, accommodation facilities, restaurant facilities, information facilities, number of accommodations and travel time of tourist destinations. The grouping of tourist destinations uses one of the k-means machine learning clustering algorithms, where the iteration process is only up to the 2nd iteration process. This study resulted in a group of 21 integrated tourist destinations, and 48 destinations that were not integrated. The results of this study can be used as a reference for tourists and also travel service actors in compiling tour packages.