Combination of Analytic Hierarchy Process and Simple Additive Weighting for Tourist Attractions Recommendation System

Abstract

The selection of the right tourist attractions is always done by tourists before visiting tourist attractions. Tourists have different criteria in choosing the tourist attractions they want to visit. There are many good tourist attractions on the island of Lombok, Indonesia, but of the many tourist attractions, tourists need recommendations for the best tourist attractions to visit. Decision-making methods can be used to create a ranking system. Analytical Hierarchical Process (AHP) is a decision-making method in Multi-Criteria Decision Making (MCDM) problems by combining qualitative and quantitative factors in complex problems. Simple Additive Weighting (SAW) is a decision-making method to generate a rating preference value. The purpose of this paper is to utilize a combination of AHP-SAW to decide the weight of the criteria and the significance of alternative tourist attractions. The results of the calculation of the AHP-SAW combination resulted in the ranking of the best tourist attractions. This study uses five alternative tourist attractions on the island of Lombok, namely Pink Beach (A1), Senggigi Beach (A2), Tanjung Aan Beach (A3), Marese Hill (A4), and Mayura Park (A5) taken fro m several trusted sites. In addition, the five criteria used are visitor reviews, visitor ratings, ticket prices, the distance of tourist attractions fro m the airport, and visiting time. The results showed that the AHP-SAW combination resulted in a consistent ratio value of 0.0371 so that the criterion-weighted data could be used as a basis for calculating the preference value and ranking of alternative tourist attractions. The best alternative for tourist attractions is Tanjung Aan Beach (A3) with a preference value of 0.9554.

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Published
2022-07-02
How to Cite
HERNAWATI, Elis; SARI, Siska Komala; WIJAYA, Dedy Rahman. Combination of Analytic Hierarchy Process and Simple Additive Weighting for Tourist Attractions Recommendation System. IJAIT (International Journal of Applied Information Technology), [S.l.], p. 123-131, july 2022. ISSN 2581-1223. Available at: <//journals.telkomuniversity.ac.id/ijait/article/view/4472>. Date accessed: 03 may 2024. doi: https://doi.org/10.25124/ijait.v5i02.4472.
Section
Articles

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