N-Days Tourist Route Recommender System in Yogyakarta Using Genetic Algorithm Method

Muhammad Ridha Anshari
Z. K. A. Baizal - [ http://orcid.org/0000-0003-0795-9559 ]


DOI: https://doi.org/10.29100/jipi.v8i3.3893

Abstract


Tourism is one of the proven solutions for the Indonesian economy. Tourism in certain regions, such as Yogyakarta, can significantly affect the region's economic development, including creating new jobs, creating new business opportunities, and increasing regional income. However, for tourists from outside Yogyakarta, it requires planning a tour before traveling in Yogyakarta, especially if he wants to spend several days on a tour. Many previous studies have developed systems that can recommend tourist routes, but not within a few days of tourist visits. In this study, we propose the use of Genetic Algorithm (GA) for automatically generating optimal travel itinerary for some days visit (n-days tour route). We develop the recommender system by combining GA and the concept of Multi-Attribute Utility Theory (MAUT). This MAUT used for accommodating user needs based some criteria such as rating, cost, and time. Based on our experimental results, GA is optimal in terms of execution time and number of attractions visited in n-days visit. The average execution time obtained is 59.62%, and the average number of attractions visited obtained is 45.95%. These results show that this method can generate tourist routes efficiently.

Keywords


recommender system, optimal travel route, genetic algorithm, multi attribute utility theory

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M. Tenemaza, S. Luján-Mora, A. de Antonio, and J. Ramírez, “Improving Itinerary Recommendations for Tourists Through Metaheu-ristic Algorithms: An Optimization Proposal,” IEEE Access, vol. 8, pp. 79003–79023, 2020, doi: 10.1109/ACCESS.2020.2990348.

A. Wicaksono, “New normal pariwisata yogyakarta,” Kepariwisataan: Jurnal Ilmiah, vol. 14, no. 3, pp. 139–150, 2020.

N. Hanafiah, I. Wijaya, S. Xavier, C. G. Young, D. Adrianto, and M. Shodiq, “Itinerary recommendation generation using enhanced simulated annealing algorithm,” Procedia Comput Sci, vol. 157, pp. 605–612, 2019.

R. W. Dewantoro, P. Sihombing, and others, “The combination of ant colony optimization (ACO) and tabu search (TS) algorithm to solve the traveling salesman problem (TSP),” in 2019 3rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), 2019, pp. 160–164.

L. Wang, R. Cai, M. Lin, and Y. Zhong, “Enhanced list-based simulated annealing algorithm for large-scale traveling salesman prob-lem,” IEEE Access, vol. 7, pp. 144366–144380, 2019.

Y. Wang and Z. Han, “Ant colony optimization for traveling salesman problem based on parameters optimization,” Appl Soft Comput, vol. 107, p. 107439, 2021.

P. M. Hariyadi, P. T. Nguyen, I. Iswanto, and D. Sudrajat, “Traveling salesman problem solution using genetic algorithm,” Journal of Critical Reviews, vol. 7, no. 1, pp. 56–61, 2020.

L. S. Hasan, “Artificial Bee Colony Algorithm and Bat Algorithm for Solving Travel Salesman Problem,” Webology, vol. 19, no. 1, pp. 4185–4193, Jan. 2022, doi: 10.14704/web/v19i1/web19276.

M. Anranur Uwaisy, Z. K. A. Baizal, and M. Yusza Reditya, “Recommendation of scheduling tourism routes using tabu search meth-od (case study bandung),” in Procedia Computer Science, 2019, vol. 157, pp. 150–159. doi: 10.1016/j.procs.2019.08.152.

Z. K. A. Baizal, K. M. Lhaksmana, A. A. Rahmawati, M. Kirom, and Z. Mubarok, “Travel route scheduling based on user’s prefer-ences using simulated annealing,” International Journal of Electrical and Computer Engineering, vol. 9, no. 2, pp. 1275–1287, 2019, doi: 10.11591/ijpeds.v9i2.pp1275-1287.

V. Chahar, S. Katoch, and S. Chauhan, “A Review on Genetic Algorithm: Past, Present, and Future,” Multimed Tools Appl, vol. 80, Jan. 2021, doi: 10.1007/s11042-020-10139-6.

R. T. Prasetio, “Genetic Algorithm to Optimize k-Nearest Neighbor Parameter for Benchmarked Medical Datasets Classification,” Jurnal Online Informatika, vol. 5, no. 2, pp. 153–160, 2020.

B. S. Wibowo and M. Handayani, “A Genetic Algorithm for Generating Travel Itinerary Recommendation with Restaurant Selection,” in 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2018, pp. 427–431. doi: 10.1109/IEEM.2018.8607677.

I. W. A. K. Yoga et al., “Advanced Traveler Information Systems: Itinerary Optimisation Using Orienteering Problem Model and Ge-netic Algorithm,” in 2018 International Conference on Information Technology Systems and Innovation (ICITSI), 2018, pp. 454–459. doi: 10.1109/ICITSI.2018.8695952.

L. Xin, P. Xu, and G. Manyi, “Logistics distribution route optimization based on genetic algorithm,” Comput Intell Neurosci, vol. 2022, 2022.

P. Yochum, L. Chang, G. Tianlong, M. Zhu, and H. Chen, “A Genetic Algorithm for Travel Itinerary Recommendation with Mandato-ry Points-of-Interest,” 2020, pp. 133–145. doi: 10.1007/978-3-030-46931-3_13.

Sunardi, Rusydi Umar, and D. Sahara, “Best Employee Decision Using Multi Attribute Utility Theory Method,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 6, pp. 945–951, Dec. 2022, doi: 10.29207/resti.v6i6.4318.

H. J. Jun, J. H. Kim, D. Y. Rhee, and S. W. Chang, “‘SeoulHouse2Vec’: An embedding-based collaborative filtering housing recom-mender system for analyzing housing preference,” Sustainability, vol. 12, no. 17, p. 6964, 2020.

S. Mirjalili, “Genetic Algorithm,” in Evolutionary Algorithms and Neural Networks: Theory and Applications, Cham: Springer Interna-tional Publishing, 2019, pp. 43–55. doi: 10.1007/978-3-319-93025-1_4.

S. Prayudani, A. Hizriadi, E. Nababan, and S. Suwilo, “Analysis Effect of Tournament Selection on Genetic Algorithm Performance in Traveling Salesman Problem (TSP),” J Phys Conf Ser, vol. 1566, p. 12131, Jan. 2020, doi: 10.1088/1742-6596/1566/1/012131.