Classification of Gen-Z Fashion Trends Based on Tiktok Social Media Activities Using the K-Means Clustering Method
DOI:
https://doi.org/10.31605/jomta.v8i1.6302Keywords:
Clustering K-Means, Gen-Z, TikTok, tren fashion, web scrapingAbstract
Fashion trends continue to evolve as times, culture and technology change. With social media playing a big role in its spread, especially among Generation Z (Gen-Z). This study aims to classify Gen-Z dressing trends based on their activities on TikTok using the K-Means Clustering method. Data was collected through web scraping techniques from the TikTok platform, including variables such as the number of likes, comments, shares, saves, and fashion-related hashtags. The clustering results showed three main clusters: cluster 1 consists of posts with very high engagement and viral tendencies, dominated by scene trends supported by major influencers. Cluster 2 has medium engagement with still dominant scene trends but comes from medium-sized accounts. Meanwhile, cluster 3 consists of posts with low engagement, dominated by casual styles that don't attract much attention. Overall, the results show that the success of Gen-Z dressing trends on social media is influenced by visual factors, the role of influencers, and interactive elements in the content.
References
[1] N. Wukirsari and A. K. A. Nugraha, “The influence of TikTok social media commerce promotional content and viral marketing on buying interest in Corkcicle products with the lifestyle of Gen Y teenagers and Gen Z as moderating variables,” vol. 8, no. 2, pp. 4779–4799, 2025.
[2] S. Pillay and I. Struweg, “Brand Ebfagement adn Loyalty On TikTOk:#BeautyBrands,” pp. 1–24, 2025.
[3] R. Nurjaman, E. T. Tosida, and B. H. Situmorang, “Inventory Replacement Decision Support System Using Clustering and Analytical Hierarchy Process ( AHP ) Methods,” vol. 6, no. 4, pp. 547–559, 2025.
[4] Y. N. Febianti and G. Nawangsari, “Applying Non-Verbal Reinforcement in Increasing Students ’ Confidence in Economic Subjects,” vol. 57, pp. 263–275, 2024.
[5] A. R. Rizalde and H. A. Mubarak, “Comparison of K-Means , BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data,” vol. 1, no. January, pp. 102–108, 2024.
[6] A. Uddin, A. Talukder, R. Ahmed, and A. Khraisat, “Data-driven strategies for digital native market segmentation using clustering,” vol. 5, no. May, pp. 178–191, 2024.
[7] R. Camelia and A. Rohman, “Analysis of the Impact of Fast Fashion and Thrifting on the Local Textile Industry and the Environment_ A Consumer Market Segmentation Perspective in Indonesia.pdf.” 2025.
[8] N. Hardiyanto, H. Lakshi, K. Perera, and L. Kusdibyo, “Customer Purchase Intentions on Hijab Fashion : The Role of Social Media Marketing Instagram and Product Quality,” vol. 2, no. 2, pp. 138–148, 2020, doi: 10.35313/ijabr.v2i02.108.
[9] W. Kurniawan and K. Kadir, “International Trade Price Index: A Leading Indicator for Indonesia’s Inflation?,” Econ. Dev. Anal. J., vol. 12, no. 2, pp. 182–193, 2023, doi: 10.15294/edaj.v12i2.63088.
[10] R. Ismail, H. Retnawati, and O. R. Imawan, “REID ( Research and Evaluation in Education ) Cluster analysis of the national examination : School grouping to maintain the sustainability of high school quality Cluster analysis of the national examination : School grouping to maintain the sustainability of high school quality,” vol. 8, no. 1, 2022.
[11] D. Rima, R. Putri, N. Ulinnuha, and P. K. Intan, “Comparison of Linkage Methods in Hierarchical Clustering for Grouping Districts / Cities in East Java Based on Stunting Determinants,” vol. 9, no. 5, pp. 2434–2442, 2025.
[12] Dahnial, “Implementation of K-Means Clustering Method to Lecturers Based on Publications of National Journals and Accredited SINTA,” vol. 8, no. 1, pp. 27–40, 2023.
[13] M. U. Siregar, “Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering,” vol. 7, no. 2, pp. 91–99, 2022.
[14] M. Hilman, A. R. Dikananda, and A. Rifai, “K-Means Algorithm for Clustering High-Achieving Student at Madrasah Tsanawiyah Yami Waled,” vol. 4, no. 3, 2025.
[15] R. Fitri, D. D. Kusumaningrum, and M. I. Irfany, “The Determinants of Fast Fashion Consumption Among Generation-Z Muslim Consumers in Indonesia,” vol. 7, no. 2, 2025.
[16] J. Malig-on and K. Campos, “Sustainability in Fast Fashion : A Study on the Understanding of Youth Consumers,” vol. 4, no. 1, 2024.
[17] O. Y. Putri, “The Implementation of Promotional Strategies via TikTok Social Media in Increasing Sales Revenue at Fat Hunt Surabaya,” vol. 8, no. 3, pp. 9085–9098, 2025.
[18] M. T. Amali, M. S. Farohi, and U. A. Dahlan, “Examining # RacunTikTok Content and Peer Group Influence on Gen Z Impulsive Buying,” no. 204, pp. 556–569, 2025, doi: 10.32509/wacana.v24i2.4977.
[19] A. S. Indra Buana Pradipta, Reiza D. Dienaputra, “Fashion Streetwear as a Support for Indie Musicians in Bandung city,” vol. 05, no. 1, 2021.
[20] W. M. Bahari, A. P. Nabila, C. L. Budiarto, R. Hidayat, and I. Respati, “The Role of Peers in Shaping Purchasing Decisions of Fashion Products among Teenagers,” vol. 2, no. 2, pp. 1–12, 2025.
[21] P. M. H. Emma Romasta Naulina Nainggolan, “Comparison and Evaluation of Euclidean Distance and Dice Distance in the K-Means Adaptive Algorithm for Clustering Composite Indexes of Food Security and Vulnerability Maps,” vol. 3, no. 02, pp. 106–118, 2025, doi: 10.58471/Science.v3i02.
[22] A. Rizky, N. Habibi, A. Maulana, H. Talirongan, and A. G. Abao, “Data Analysis of Student Monitoring Using the K-Means Clustering Method,” vol. 1, no. 2, pp. 50–57, 2025.
[2] S. Pillay and I. Struweg, “Brand Ebfagement adn Loyalty On TikTOk:#BeautyBrands,” pp. 1–24, 2025.
[3] R. Nurjaman, E. T. Tosida, and B. H. Situmorang, “Inventory Replacement Decision Support System Using Clustering and Analytical Hierarchy Process ( AHP ) Methods,” vol. 6, no. 4, pp. 547–559, 2025.
[4] Y. N. Febianti and G. Nawangsari, “Applying Non-Verbal Reinforcement in Increasing Students ’ Confidence in Economic Subjects,” vol. 57, pp. 263–275, 2024.
[5] A. R. Rizalde and H. A. Mubarak, “Comparison of K-Means , BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data,” vol. 1, no. January, pp. 102–108, 2024.
[6] A. Uddin, A. Talukder, R. Ahmed, and A. Khraisat, “Data-driven strategies for digital native market segmentation using clustering,” vol. 5, no. May, pp. 178–191, 2024.
[7] R. Camelia and A. Rohman, “Analysis of the Impact of Fast Fashion and Thrifting on the Local Textile Industry and the Environment_ A Consumer Market Segmentation Perspective in Indonesia.pdf.” 2025.
[8] N. Hardiyanto, H. Lakshi, K. Perera, and L. Kusdibyo, “Customer Purchase Intentions on Hijab Fashion : The Role of Social Media Marketing Instagram and Product Quality,” vol. 2, no. 2, pp. 138–148, 2020, doi: 10.35313/ijabr.v2i02.108.
[9] W. Kurniawan and K. Kadir, “International Trade Price Index: A Leading Indicator for Indonesia’s Inflation?,” Econ. Dev. Anal. J., vol. 12, no. 2, pp. 182–193, 2023, doi: 10.15294/edaj.v12i2.63088.
[10] R. Ismail, H. Retnawati, and O. R. Imawan, “REID ( Research and Evaluation in Education ) Cluster analysis of the national examination : School grouping to maintain the sustainability of high school quality Cluster analysis of the national examination : School grouping to maintain the sustainability of high school quality,” vol. 8, no. 1, 2022.
[11] D. Rima, R. Putri, N. Ulinnuha, and P. K. Intan, “Comparison of Linkage Methods in Hierarchical Clustering for Grouping Districts / Cities in East Java Based on Stunting Determinants,” vol. 9, no. 5, pp. 2434–2442, 2025.
[12] Dahnial, “Implementation of K-Means Clustering Method to Lecturers Based on Publications of National Journals and Accredited SINTA,” vol. 8, no. 1, pp. 27–40, 2023.
[13] M. U. Siregar, “Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering,” vol. 7, no. 2, pp. 91–99, 2022.
[14] M. Hilman, A. R. Dikananda, and A. Rifai, “K-Means Algorithm for Clustering High-Achieving Student at Madrasah Tsanawiyah Yami Waled,” vol. 4, no. 3, 2025.
[15] R. Fitri, D. D. Kusumaningrum, and M. I. Irfany, “The Determinants of Fast Fashion Consumption Among Generation-Z Muslim Consumers in Indonesia,” vol. 7, no. 2, 2025.
[16] J. Malig-on and K. Campos, “Sustainability in Fast Fashion : A Study on the Understanding of Youth Consumers,” vol. 4, no. 1, 2024.
[17] O. Y. Putri, “The Implementation of Promotional Strategies via TikTok Social Media in Increasing Sales Revenue at Fat Hunt Surabaya,” vol. 8, no. 3, pp. 9085–9098, 2025.
[18] M. T. Amali, M. S. Farohi, and U. A. Dahlan, “Examining # RacunTikTok Content and Peer Group Influence on Gen Z Impulsive Buying,” no. 204, pp. 556–569, 2025, doi: 10.32509/wacana.v24i2.4977.
[19] A. S. Indra Buana Pradipta, Reiza D. Dienaputra, “Fashion Streetwear as a Support for Indie Musicians in Bandung city,” vol. 05, no. 1, 2021.
[20] W. M. Bahari, A. P. Nabila, C. L. Budiarto, R. Hidayat, and I. Respati, “The Role of Peers in Shaping Purchasing Decisions of Fashion Products among Teenagers,” vol. 2, no. 2, pp. 1–12, 2025.
[21] P. M. H. Emma Romasta Naulina Nainggolan, “Comparison and Evaluation of Euclidean Distance and Dice Distance in the K-Means Adaptive Algorithm for Clustering Composite Indexes of Food Security and Vulnerability Maps,” vol. 3, no. 02, pp. 106–118, 2025, doi: 10.58471/Science.v3i02.
[22] A. Rizky, N. Habibi, A. Maulana, H. Talirongan, and A. G. Abao, “Data Analysis of Student Monitoring Using the K-Means Clustering Method,” vol. 1, no. 2, pp. 50–57, 2025.
Published
2026-04-30
Issue
Section
Articles
