Analysis and Design of a Route Recommendation System and Bicycle Rental Fees at Tourist Destinations with Genetic Algorithms

Danu Fawwaz Gimnastian, Rahmat Yasirandi, Dita Oktaria

Abstract


The tourism sector is one of the country's most significant sources of income, especially in Indonesia. This is inseparable from many visitors or foreign tourists who come to Indonesia. Bandung is a tourist destination that is famous for its natural beauty and traditional culture, one example is in Desa Alamendah which has several tourist destinations, namely agro-tourism, nature tourism, and cultural tourism. However, access that can be passed from every tourist spot in Desa Alamendah can only be given on foot or by bicycle. Responding to this matter, bicycle facilities as a means of rotation are needed to access tourist attractions in Desa Alamendah. Based on these problems, this study aims to create a recommendation system for tourist attractions and bicycle rentals using an application in Desa Alamendah, Bandung. Using the Genetic Algorithm, an algorithm that can solve multi-objective problems can be applied to a tourist spot recommendation system in Desa Alamendah. In this system, the Genetic Algorithm is also used to determine fares based on the total distance traveled, so that bicycle rentals are more efficient. The study combines Google Maps as an appropriate route selection based on the recommendation of tourist destination points that tourists can visit using Genetic Algorithms, so that bicycle rentals are more efficient. This is study combines Google Maps as an appropriate route selection based on the recommendation of tourist destination points that tourists can visit using Genetic Algorithms. So that bicycle rentals are more efficient. The study combines Google Maps as an appropriate route selection based on the recommendation of tourist destination points that tourists can visit using Genetic Algorithms. Based on the results of tests that have been carried out in the process of forming tourist attractions recommendations with a Genetic Algorithm that using mutation probability 1.0 and Crossover Probability 0.6, can produce a Mobile Application with a Genetic Algorithm as a tourist spot recommendation system in Desa Alamendah. In addition, it also can provide recommendations, namely displaying tourist points, route using Google Maps. The rental fee is based on the total distance traveled.

Keywords


Genetic Algorithm; Recommendation System; Tourist Attractions; Google Maps

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DOI: https://doi.org/10.30865/mib.v6i2.3749

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