Algoritma K-Means Untuk Clustering Rute Perjalanan Wisata Pada Agen Tour & Travel

Abstract

Government support for the development of tourism has an impact on the growth of business opportunities for travel agents. Along with the advancement of the domestic travel sector, tour & travel agent business forms have sprung up that influence business competition between travel agents. The problem with tour & travel agents is the lack of information about tourist routes that are most in-demand by customers. To solve this problem the method used to classify the most desirable travel routes using the method of data mining is clustering with the K-Means algorithm. Based on the results of the study found three groups of travel routes, namely the most desirable travel routes by 20%, the trips that are in demand by 30% and less desirable trips by 50%.