Open AccessAlgoritma K-Means Untuk Clustering Rute Perjalanan Wisata Pada Agen Tour & Travel
Author(s) -
Eni Irfiani,
Fintri Indriyani
Publication year - 2020
Publication title -
indonesian journal of computer science/indonesian journal of computer science
Language(s) - English
Resource type - Journals
eISSN - 2549-7286
pISSN - 2302-4364
DOI - 10.33022/ijcs.v9i1.244
Subject(s) - trips architecture , tourism , cluster analysis , business , transport engineering , government (linguistics) , travel behavior , competition (biology) , business travel , transit (satellite) , computer science , operations research , geography , engineering , public transport , artificial intelligence , biology , ecology , linguistics , philosophy , archaeology
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%.