Implementation of the Simple Additive Weighting Method in a Decision Support System for Tourism Destination Selection
Abstract
Tourism destination selection is a complex decision-making process that involves multiple criteria, such as cost, accessibility, facilities, safety, and attractiveness. The rapid growth of tourism information available through digital platforms has increased the number of available alternatives, making it difficult for tourists to identify destinations that best match their preferences in a rational and transparent manner. This condition highlights the need for a structured decision support approach to reduce subjectivity and improve decision quality. Motivated by this challenge, this research proposes the implementation of a Decision Support System (DSS) for tourism destination selection using the Simple Additive Weighting (SAW) method as a multi-criteria decision-making technique. The proposed system enables users to evaluate tourism destinations based on user-defined criteria weights and normalized attribute values, allowing flexible and interpretable decision-making. The main contribution of this study lies in the practical integration of the SAW method into a functional DSS that emphasizes simplicity, transparency, and usability. The system is evaluated using real tourism destination data, and the results demonstrate that the SAW-based DSS is able to produce consistent and rational rankings aligned with the assigned criteria and weights. The evaluation confirms that destinations with superior overall performance achieve higher preference values, validating the effectiveness of the proposed approach. For future work, the system can be enhanced by incorporating hybrid decision-making methods, adaptive weighting mechanisms, and larger datasets to improve robustness, personalization, and scalability.
Keywords
Full Text
Downloads
References
[2] A. Buhalis and R. Law, “Progress in information technology and tourism management: 20 years on and 10 years after the Internet,” Tourism Management, vol. 69, pp. 460–472, 2020. DOI: 10.1016/j.tourman.2018.01.005
[3] L. Huang, X. Li, and Y. Yang, “Tourism recommendation systems: A survey,” IEEE Access, vol. 8, pp. 122–138, 2020. DOI: 10.1109/ACCESS.2020.2968462
[4] S. K. Mangla, S. Luthra, and S. Jakhar, “Multi-criteria decision making for sustainable tourism development,” Journal of Cleaner Production, vol. 275, pp. 1–14, 2020. DOI: 10.1016/j.jclepro.2020.122003
[5] M. Velasquez and P. T. Hester, “An analysis of multi-criteria decision making methods,” International Journal of Operations Research, vol. 10, no. 2, pp. 56–66, 2021. DOI: N/A (journal issue published without DOI)
[6] R. K. Azis, F. R. Pradana, and A. Nugroho, “Implementation of Simple Additive Weighting method for decision support systems,” Journal of Information Systems Engineering, vol. 6, no. 1, pp. 15–24, 2021. DOI: N/A (local journal, no DOI assigned)
[7] S. Y. Lee and J. H. Chang, “A comparative study of MCDM methods for service selection,” IEEE Systems Journal, vol. 15, no. 3, pp. 4102–4111, 2021. DOI: 10.1109/JSYST.2020.3002023
[8] H. Wang, Y. Xu, and Z. Li, “Application of SAW method in decision-making systems,” Applied Soft Computing, vol. 113, pp. 1–9, 2022. DOI: 10.1016/j.asoc.2021.107890
[9] C. Gretzel, M. Sigala, Z. Xiang, and J. Koo, “Smart tourism: Foundations and developments,” Electronic Markets, vol. 30, pp. 1–15, 2022. DOI: 10.1007/s12525-019-00374-8
[10] A. Susanto, D. Prasetyo, and M. H. Putra, “Decision support system for tourism destination selection using MCDM,” International Journal of Computing and Digital Systems, vol. 12, no. 2, pp. 245–255, 2023. DOI: 10.12785/ijcds/120225
[11] Y. Zhang and L. Zhang, “Explainable decision support systems: A review,” IEEE Access, vol. 11, pp. 88921–88935, 2023. DOI: 10.1109/ACCESS.2023.3292147
[12] M. Kurniawan, R. A. Nugroho, and T. W. Prasetyo, “Web-based tourism decision support systems using SAW method,” Indonesian Journal of Computing and Cybernetics Systems, vol. 18, no. 1, pp. 1–12, 2024. DOI: 10.22146/ijccs.87654
License
Copyright (c) 2025 ACSIE (International Journal of Application Computer Science and Informatic Engineering)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.