Analisis Hasil Implementasi Multi-Attribute Utility Theory (MAUT) dalam Pengemasan Paket Wisata Tematik
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Yerik Afrianto Singgalen
*
Atma Jaya Catholic University of Indonesia, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
The travel industry plays a vital role in maximizing the marketing of tourist destinations, but the process of determining tour packages must consider consumer purchasing power concerning destination ticket prices, distance and travel time, availability of accommodations and amenities services, and regulations. This study seeks to use the Multi-Attribute Utility Theory (MAUT) decision support model to tourist case studies from Ternate City to determine superior tour packages. In the meantime, the context of destinations, accommodation services, and transportation services is incorporated into the use of the MAUT decision support model. The following criteria are established based on the category of the location: entrance fee; facilities and infrastructure; local tour guides; type of activity at the destination; Security, and Hygiene. The following criteria are established based on the category of lodging services: standard room rate, property amenities, room features, room type, and services. In addition, the criteria established based on the category of transportation services are as follows: rental pricing; car type; vehicle amenities; driver experience. The findings of this study indicate that A5 tourist destinations are recommended, with a total value of 0.90, based on destination category, criteria and criteria values related to ticket prices (10), facilities and infrastructure (20), availability of local tour guides (10), diversity of activities (20), safety (20), and cleanliness (20). In addition, based on the criteria and weights related to standard room rental costs (20), property amenities (20), room features (20), room type (10), and services (30), we propose A1 with a total value of 0.85 in the accommodation services category. In the field of transportation services, we offer A2 with a total score of 0.83 based on criteria and weights relating to rental price (25), vehicle type (25), car amenities (25), and driver experience (25). Using the MAUT decision support model, it is evident that the packaging of tour bundling becomes more effective and efficient
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