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

Eni Irfiani | Zendy; Fintri Indriyani | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Algoritma 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

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%.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research