Implementation K-Means Algorithm in Promotional Media Destination Tour Minahasa Web Based
Abstract
Minahasa Regency has great tourism potential with a variety of destinations including cultural, natural, and man-made tourism. However, tourism promotion efforts still face obstacles due to the lack of integrated media capable of grouping destination information based on tourist interests and preferences. This study aims to apply the K-Means clustering algorithm in web-based promotional media to group Minahasa tourist destinations based on the level of user interaction, which is represented by the number of likes and comments on promotional content for each destination. The research method is carried out through several stages, namely collecting tourist destination data, pre-processing interaction data, implementing the K-Means algorithm with a specified number of clusters of three according to the main categories of tourismcultural, natural, and man-made), and implementing the clustering results into a web-based evaluation system that uses the Silhouette Coefficient to evaluate the quality of cluster formation. The results show that the K-Means algorithm is able to effectively group tourist destinations into three clusters that reflect the level of popularity, making it easier for users to find destination recommendations according to their interests. Implementation in a web-based system also provides an interactive display in the form of a list of destinations per cluster and recommendations for popular destinations. Thus, this study proves that the application of K-Means can increase the effectiveness of Minahasa tourism promotion, and in the future it can be developed with the integration of real-time data from social media and comparison with other clustering algorithms.
References
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295. https://doi.org/10.3390/electronics9081295
Alsahafi, R., Alzahrani, A., & Mehmood, R. (2023). Smarter sustainable tourism: data-driven multi-perspective parameter discovery for autonomous design and operations. Sustainability, 15(5), 4166. https://doi.org/10.3390/su15054166
Artamevia, M., Lubis, M., Mukti, I. Y., & Handayani, D. (2025). Analyzing User Needs and Recommending Targeted Features for Bi’ih Village Tourism Website Using Text Mining and K-Means Clustering. Jurnal Teknik Informatika (Jutif), 6(6), 5609-5620.
Briedenhann, J., & Wickens, E. (2004). Tourism routes as a tool for the economic development of rural areas—vibrant hope or impossible dream?. Tourism management, 25(1), 71-79. https://doi.org/10.1016/S0261-5177(03)00063-3
Cassisi, C., Ferro, A., Giugno, R., Pigola, G., & Pulvirenti, A. (2013). Enhancing density-based clustering: Parameter reduction and outlier detection. Information Systems, 38(3), 317-330. https://doi.org/10.1016/j.is.2012.09.001
Damos, M. A., Zhu, J., Li, W., Khalifa, E., Hassan, A., Elhabob, R., ... & Ei, E. (2024). Enhancing the K-means algorithm through a genetic algorithm based on survey and social media tourism objectives for tourism path recommendations. ISPRS International Journal of Geo-Information, 13(2), 40. https://doi.org/10.3390/ijgi13020040
Fuchs, M., Höpken, W., & Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations–A case from Sweden. Journal of destination marketing & management, 3(4), 198-209. https://doi.org/10.1016/j.jdmm.2014.08.002
Gordon, J. E. (2018). Geoheritage, geotourism and the cultural landscape: Enhancing the visitor experience and promoting geoconservation. Geosciences, 8(4), 136. https://doi.org/10.3390/geosciences8040136
Haraty, R. A., Dimishkieh, M., & Masud, M. (2015). An enhanced k-means clustering algorithm for pattern discovery in healthcare data. International Journal of distributed sensor networks, 11(6), 615740. https://doi.org/10.1155/2015/615740
Katarya, R., & Verma, O. P. (2017). An effective web page recommender system with fuzzy c-mean clustering. Multimedia Tools and Applications, 76(20), 21481-21496. https://doi.org/10.1007/s11042-016-4078-7
Katelieva, M., & Muhar, A. (2022). Heritage tourism products based on traditional nature-related knowledge: assessment of cultural, social, and environmental factors in cases from rural Austria. Journal of Heritage Tourism, 17(6), 631-647. https://doi.org/10.1080/1743873X.2022.2098040
Khasanah, U., Pratama, C. D. Y., Firlita, F., & Saputra, K. S. (2024). Upaya Meningkatkan Promosi Wisata Melalui Pembuatan Media Promosi Digital Di Kampung Wisata Ketandan. Jurnal Pengabdian Mandiri, 3(1), 71-78. https://doi.org/10.53625/jpm.v3i1.7288
Kim, K. J., & Ahn, H. (2008). A recommender system using GA K-means clustering in an online shopping market. Expert systems with applications, 34(2), 1200-1209. https://doi.org/10.1016/j.eswa.2006.12.025
Kraj, P., Sharma, A., Garge, N., Podolsky, R., & McIndoe, R. A. (2008). ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use. BMC bioinformatics, 9(1), 200. https://doi.org/10.1186/1471-2105-9-200
Liu, Z., Wang, A., Weber, K., Chan, E. H., & Shi, W. (2022). Categorisation of cultural tourism attractions by tourist preference using location-based social network data: The case of Central, Hong Kong. Tourism Management, 90, 104488. https://doi.org/10.1016/j.tourman.2022.104488
Llodra-Riera, I., Martínez-Ruiz, M. P., Jiménez-Zarco, A. I., & Izquierdo-Yusta, A. (2015). Assessing the influence of social media on tourists’ motivations and image formation of a destination. International Journal of Quality and Service Sciences, 7(4), 458-482. https://doi.org/10.1108/IJQSS-03-2014-0022
Minanda, H., Ulya, B. N., Budiatiningsih, M., Anggara, A., & Suryani, J. (2024). Analisis Implementasi Digital Tourism sebagai Upaya Promosi Pariwisata di Desa Wisata Kembang Kuning Lombok Timur. Jurnal Ilmiah Hospitality, 13(2), 291-300. https://doi.org/10.53625/jpm.v3i1.7288
Narangajavana Kaosiri, Y., Callarisa Fiol, L. J., Moliner Tena, M. Á., Rodríguez Artola, R. M., & Sánchez García, J. (2019). User-generated content sources in social media: A new approach to explore tourist satisfaction. Journal of Travel Research, 58(2), 253-265. https://doi.org/10.1177/0047287517746014
Pancras, J., & Sudhir, K. (2007). Optimal marketing strategies for a customer data intermediary. Journal of Marketing research, 44(4), 560-578. https://doi.org/10.1509/jmkr.44.4.560
Petroman, C. (2015). Typology of tourism destinations. Scientific Papers Animal Science and Biotechnologies, 48(1), 338-338.
Puspawati, D. P. H., & Ristanto, R. (2018). Strategi promosi digital untuk pengembangan pariwisata Kota Magelang. Jurnal Jendela Inovasi Daerah, 1(2), 1-20. https://doi.org/10.56354/jendelainovasi.v1i2.14
Ramadhan, A., Achmad, F., Zulkarnain, I., & Aritsugi, M. (2025). Evaluation of K-Means, DBSCAN, and Hierarchical Clustering for Strategic Segmentation of Tourism SMEs in Rembang, Indonesia. Jurnal Teknik Informatika (Jutif), 6(3), 1605-1630. https://doi.org/10.52436/1.jutif.2025.6.3.4602
Raykov, Y. P., Boukouvalas, A., Baig, F., & Little, M. A. (2016). What to do when K-means clustering fails: a simple yet principled alternative algorithm. PloS one, 11(9), e0162259. https://doi.org/10.1371/journal.pone.0162259
Ricca, F., Pianta, E., Tonella, P., & Girardi, C. (2008). Improving Web site understanding with keyword‐based clustering. Journal of Software Maintenance and Evolution: Research and Practice, 20(1), 1-29. https://doi.org/10.1002/smr.360
Rogerson, C. M. (2006). Pro-poor local economic development in South Africa: The role of pro-poor tourism. Local environment, 11(1), 37-60. https://doi.org/10.1080/13549830500396149
Sardak, S. E., Krupskyi, O. P., Dzhyndzhoian, V., Sardak, M., & Naboka, Y. (2020). Development of historical and cultural tourist destinations. Journal of Geology, Geography and Geoecology, 29(2), 406-414.
Sarker, K. U., Saqib, M., Hasan, R., Mahmood, S., Hussain, S., Abbas, A., & Deraman, A. (2022). A ranking learning model by K-means clustering technique for web scraped movie data. Computers, 11(11), 158. https://doi.org/10.3390/computers11110158
Shrifan, N. H., Akbar, M. F., & Isa, N. A. M. (2022). An adaptive outlier removal aided k-means clustering algorithm. Journal of King Saud University-Computer and Information Sciences, 34(8), 6365-6376. https://doi.org/10.1016/j.jksuci.2021.07.003
Singh, H., & Kaur, P. (2021). An effective clustering-based web page recommendation framework for e-commerce websites. SN Computer Science, 2(4), 339. https://doi.org/10.1007/s42979-021-00736-z
Sukmana, H. T., & Oh, L. K. (2024). Using K-Means Clustering to Enhance Digital Marketing with Flight Ticket Search Patterns. Journal of Digital Market and Digital Currency, 1(3), 286-304. https://doi.org/10.47738/jdmdc.v1i3.22
Syahrin, A., & Yuwita, N. (2025). Strategi Komunikasi Digital Marketing Sebagai Promosi Destinasi Desa Wisata Kertosari Pasuruan. Al-Qolamuna: Journal Komunikasi dan Penyiaran Islam, 2(2), 241-258. https://doi.org/10.71242/0g9z7v50
Wang, L. (2024). Enhancing tourism management through big data: Design and implementation of an integrated information system. Heliyon, 10(20).
Xiong, K., Zhang, S., Fei, G., Jin, A., & Zhang, H. (2023). Conservation and sustainable tourism development of the natural world heritage site based on aesthetic value identification: a case study of the Libo Karst. Forests, 14(4), 755. https://doi.org/10.3390/f14040755
Yee, L. Y., Belaidan, S. L. M., Abd Rahman, N. A., & Harun, K. S. (2019). Implementing k-means clustering algorithm in collaborative trip advisory and planning system. Periodicals of Engineering and Natural Sciences, 7(2). https://doi.org/10.21533/pen.v7.i2.1578
Yildirim, M. E., Kaya, M., & FurkanInce, I. (2022). A case study: unsupervised approach for tourist profile analysis by k-means clustering in turkey. Journal of Internet Computing and Services, 23(1), 11-17. https://doi.org/10.7472/jksii.2022.23.1.11
Zhang, M., Xiong, K., Wang, X., & Zhao, X. (2022). Natural beauty and esthetic value of natural world heritage sites: a literature review and implications for Karst Geoheritage Sites. Geoheritage, 14(3), 84. https://doi.org/10.1007/s12371-022-00714-8
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