GDSS for Selecting Culinary Tourism in Bali Using Profile Matching and Borda

DOI https://doi.org/10.33173/jsikti.246
Abstract views: 68 , PDF downloads: 67

Putu Sugiartawan
Institut Bisnis dan Teknologi Indonesia
Ni Wayan Wardani
Okayama University
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Published Nov 11, 2025

Abstract

This research proposes a method for determining the best culinary tourism destinations in Bali. Currently, being able to determine the best and potential destinations is a problem for people who want to travel tourism in Bali, both domestic and foreign people. Factors that have a dominant influence in determining potential destinations that still cannot be determined with certainty. This will greatly affect the results of decisions that will be taken by the community in determining the destination to be chosen. For this reason, it is very important to create a model to determine the best destination that can be chosen by the community as a decision support system in making decisions. In this journal Profile Matching has been used to determine the best destination as a category that can be obtained from the rating shown on the selected destination.

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How to Cite
Sugiartawan, P., & Wardani, N. W. (2025). GDSS for Selecting Culinary Tourism in Bali Using Profile Matching and Borda. Jurnal Sistem Informasi Dan Komputer Terapan Indonesia (JSIKTI), 5(4), 295-304. https://doi.org/10.33173/jsikti.246
Issue
Vol 5 No 4 (2023): June
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

References

[1] Chen, T., & Guestrin, C. (2021). "XGBoost: A scalable tree boosting system." Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
[2] Santos, J. R., & Carvalho, R. F. (2022). "Predicting wine quality using machine learning: A focus on chemical properties." Journal of Food Science Technology, 59(3), 112-124..
[3] Smith, A. L., & Brown, P. J. (2023). "Gradient boosting in wine quality prediction: A case study with XGBoost." International Journal of Data Science, 17(2), 56-72.
[4] Kumar, S., & Patel, V. (2024). "Feature engineering for wine quality prediction: Insights from chemical datasets." Applied Machine Learning Journal, 14(1), 33-48..
[5] Kumar, S., & Patel, V. (2024). "Feature engineering for wine quality prediction: Insights from chemical datasets." Applied Machine Learning Journal, 14(1), 33-48.
[6] Li, J., & Zhou, M. (2021). "Machine learning for wine quality prediction: An overview." Journal of Food Engineering, 309, 110704.
[7] Silva, F., & Costa, P. (2022). "Exploring the relationship between chemical properties and wine quality using machine learning." Food Chemistry, 384, 132548.