Selecting Tourism Site Using 6 As Tourism Destinations Framework Based Multi-Criteria Recommender System

Yunifa Miftachul Arif, Duvan Deswantara Putra, Nauman Khan

Abstract


Batu City is a place with many types of tourism and had many tourists in 2019. However, there was an imbalance of tourist attractions visited from the total number. Tourists are only fixated on famous tourist spots. Therefore, a recommendation system is needed that can provide recommendations for tourists. In this study, we use the Multi-Criteria Recommender System (MCRS) method based on the rating value between users to obtain recommendations from the system regarding selecting tourist destinations. The authors use the 6 As Tourism Destinations (6AsTD) framework for user assessment criteria in this study. The framework consists of six indicators that assess the success of tourism destinations, including attractions, accessibility, amenities, support services, activities, and available packages. The six components are considered the key to the success of a tourist destination under the marketing approach. This study aimed to obtain a recommendation system for selecting tourist destinations using the multi-criteria concept based on the 6AsTD framework. Based on the experimental results, the proposed method has an accuracy rate of up to 72%.


Keywords


Recommendation, Multi-Criteria Recommender System, tourism destinations, 6AsTD framework

Full Text:

PDF

References


M. S. Mahfudz, Z. Arham, and E. Khudzaeva, “Development of web-based spatial information system tourism industry event distribution (case study: DKI jakarta),” Appl. Inf. Syst. Manag., vol. 3, no. 2, pp. 107–112, 2021, doi: 10.15408/aism.v3i2.17986.

N. Permatasari, “Penggunaan indeks google trend dalam peramalan jumlah pengunjung taman rekreasi selecta tahun 2020,” Semin. Nas. Off. Stat., vol. 2021, no. 1, pp. 1019–1024, 2021, doi: 10.34123/semnasoffstat.v2021i1.993.

F. Ricci, L. Rokach, and B. Shapira, Introduction to Recommender Systems Handbook. Springer Science+Business Media, 2011.

G. Adomavicius and Y. Kwon, Multi-Criteria Recommender Systems, Recommender. New York: Springer Science+Business Media, 2015.

G. Adomavicius, N. Manouselis, and Y. Kwon, Multi-Criteria Recommender Systems. 2011.

M. Hassan, “Performance analysis of neural networks-based multi-criteria recommender systems,” in 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2017, pp. 490–494.

Y. M. Arif, H. Nurhayati, S. Harini, S. M. Susiki Nugroho, and M. Hariadi, “Decentralized tourism destinations rating system using 6AsTD framework and blockchain,” in 2020 International Conference on Smart Technology and Applications (ICoSTA), 2020, pp. 1–6, doi: 10.1109/icosta48221.2020.1570614662.

Y. M. Arif, H. Nurhayati, S. M. S. Nugroho, and M. Hariadi, “Destinations ratings based multi-criteria recommender system for indonesian halal tourism game,” Int. J. Intell. Eng. Syst., vol. 15, no. 1, pp. 282–294, 2022, doi: 10.22266/ijies2022.0228.26.

E. Winarko, “Konsep multicriteria collaborative filtering” in Seminar Nasional Aplikasi Teknologi Informasi 2010 (SNATI 2010), 2010, vol. 2010, no. Snati, pp. 51–55.

Y. M. Arif, S. Harini, S. M. S. Nugroho, and M. Hariadi, “An automatic scenario control in serious game to visualize tourism destinations recommendation,” IEEE Access, vol. 9, pp. 89941–89957, 2021, doi: 10.1109/access.2021.3091425.

D. Buhalis and A. Spada, “Destination management systems : criteria for success - an exploratory research,” Inf. Technol. Tour., vol. 3, pp. 41–58, 2000.

M. Hassan and M. Hamada, “Enhancing learning objects recommendation using multi-criteria recommender systems,” Proc. 2016 IEEE Int. Conf. Teaching, Assess. Learn. Eng. TALE 2016, no. December, pp. 62–64, 2017, doi: 10.1109/TALE.2016.7851771.

N. Nassar, A. Jafar, and Y. Rahhal, “Knowledge-based systems a novel deep multi-criteria collaborative filtering model for recommendation system ✩,” Knowledge-Based Syst., vol. 187, p. 104811, 2020, doi: 10.1016/j.knosys.2019.06.019.

C. Wirawan, “teknik data mining menggunakan algoritma decision tree C4.5 untuk memprediksi tingkat kelulusan tepat waktu,” Appl. Inf. Syst. Manag., vol. 3, no. 1, pp. 47–52, 2020, doi: 10.15408/aism.v3i1.13033.

K. Niwa, "Toward successful implementation of knowledge-based systems: expert systems versus knowledge sharing systems," IEEE Transactions on Engineering Management, vol. 37, no. 4, pp. 277-283, 1990.




DOI: https://doi.org/10.15408/aism.v6i1.25140 Abstract - 0 PDF - 0

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

EDITORIAL ADDRESS:

Department of Information Systems, Faculty of Science and Technology,
Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
Faculty of Science and Technology Building, 3rd Floor, 1st Campus, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No. 95, Ciputat Timur, Kota Tangerang Selatan, Banten 15412, Indonesia.
Tlp/Fax: +622174019 25/+62217493315.
E-mail: aism.journal@apps.uinjkt.ac.id, Website: https://journal.uinjkt.ac.id/index.php/aism

Creative Commons Licence
Applied Information System and Management (AISM) by the Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, Indonesia is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://journal.uinjkt.ac.id/index.php/aism.