Identifying high-value keywords for Bali tourism: A machine learning approach

Asep Koswara

doi:10.58881/jcmts.v4i2.334

Authors

Asep Koswara IKOPIN University

DOI:

https://doi.org/10.58881/jcmts.v4i2.334

Keywords:

Bali tourism, keyword analysis, machine learning, tourism marketing, google keyword planner

Abstract

This study explores how machine learning techniques, specifically K-Means Clustering, can be applied to identify high-value tourism-related keywords for Bali using Google Keyword Planner data. By analyzing normalized search volume, cost-per-click (CPC), and competition scores, the keywords were grouped into three meaningful clusters: (1) high-volume, high-CPC keywords such as Bali luxury resorts and honeymoon in Bali, (2) low-volume, high-CPC keywords like eco-retreat Bali and private villa Ubud, and (3) high-volume, low-CPC keywords including Bali itinerary and Bali beach names. The Elbow Method confirmed that three clusters offered the optimal segmentation. Findings show that luxury and culturally immersive travel themes yield the highest marketing value, while niche keywords offer untapped advertising potential. Seasonal analysis revealed peaks in keyword interest during mid-year and year-end holiday seasons, aligning with international travel patterns. The machine learning approach enhanced keyword structuring and revealed strategic timing opportunities for content deployment. The study concludes that data-driven keyword targeting can significantly improve the effectiveness and sustainability of digital marketing in Bali’s tourism sector. Recommendations include focusing on premium and niche clusters, while future research may integrate social media trends and geospatial analysis to deepen keyword relevance and behavioral insights.

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References

Arianto, H., Pitana, I. G., Wiranatha, A. S., & Budiarsa, M. (2022). The effectiveness of Bali tourism promotion through out digital marketing videos. International Journal of Social Science, 1(5), 553-562.

Bach, M. P., Schatten, M., & Marušic, Z. (2013, September). Data mining applications in tourism: A keyword analysis. In Central European conference on information and intelligent systems (Vol. 24, p. 2013). Faculty of Organization and Informatics Varazdin.

Cenamor, I., de la Rosa, T., Núñez, S., & Borrajo, D. (2017). Planning for tourism routes using social networks. Expert Systems with Applications, 69, 1-9.

Diarta, I. K. S. (2015). The Influence of Bali Brand Equity on Tourists Traveling Behavior. E-Journal of Tourism, 2(2), 96-114.

Fauzi, D., & Sumirat, P. A. (2023). Evaluation of Indonesian Tourism Marketing towards Quality Tourism. International Journal of Magistravitae Management, 1(2), 117-129.

Google. (2025). Google Keyword Planner. Retrieved from https://ads.google.com/home/tools/keyword-planner/

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.

Koswara, A., & Marpuah, S. (2024). Designing an innovative SEO marketing plan to promote Indonesian tourism using AI-driven tool. Indonesian Tourism Journal, 1(3), 202-216.

Li, X., Li, H., Pan, B., & Law, R. (2021). Machine learning in internet search query selection for tourism forecasting. Journal of Travel Research, 60(6), 1213-1231.

Núñez, J. C. S., Gómez‐Pulido, J. A., & Ramírez, R. R. (2024). Machine learning applied to tourism: A systematic review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(5), e1549.

Sharda, R., Delen, D., & Turban, E. (2020). Business Intelligence, Analytics, and Data Science: A Managerial Perspective (4th ed.). Pearson.

Suryani, L. P., Permatasari, I., & Pitriyantini, P. E. (2023, January). The Concept of Sustainable Quality Tourism in the Development of Tourism in Bali. In 3rd International Conference on Business Law and Local Wisdom in Tourism (ICBLT 2022) (pp. 801-809). Atlantis Press.

Susanti, I. A. M. D., Pratiwi, L. P. K., & Yudiarini, N. (2022). THE POTENTIAL OF THE PEOPLE'S MARKET IN BALI AS A TOURISM ATTRACTION BASED ON BALI CULTURE TOURISM. International Journal of Applied Science and Sustainable Development (IJASSD), 4(2), 64-69.

Ustriyana, I. N. G., Saifulloh, M., Diara, I. W., & Suyarto, R. Mapping agrotourism potential using geospatial data and random forest: A case study from Bali. Ecological Engineering & Environmental Technology, 26(3).

Wang, T. L., & Azizurrohman, M. (2025). Segmenting International Tourists in Indonesia: A Cluster Analysis of Preferences, Motivations, and Behaviors. Journal of Economics Research and Social Sciences, 9(1), 93-107.

Xie, G., Qian, Y., & Wang, S. (2021). Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tourism Management, 82, 104208.