Media Literacy Strategy for Youth Generation Based on Segmentation in Jakarta using the AHP Model and K-Means Clustering
DOI:
https://doi.org/10.9744/nirmana.26.1.12-21Keywords:
Media literacy, youth, integrative model, Analytic Hierarchy Process (AHP), K-Means ClusteringAbstract
The high digital penetration in Jakarta has not been matched by adequate media literacy, particularly among the younger generation. This study proposes an integrative model combining the Analytical Hierarchy Process (AHP) and K-Means Clustering to design a data-driven and behaviorally segmented media literacy strategy. Data were collected through in-depth interviews with 15 practitioners and experts, as well as a survey of 202 young respondents aged 15–24 from five regions of Jakarta. The AHP approach was used to identify priority dimensions of media literacy, encompassing cognitive, technical, ethical, and participatory aspects. K-Means clustering was employed to group individuals based on their digital competency characteristics and to empirically map literacy segmentation patterns. The results reveal three main clusters with significant differences in media usage patterns, ethical awareness, and critical capacities. These findings confirm the importance of adaptive and evidence-based media literacy strategies, rather than merely normative interventions. This integrative model is expected to form the basis for policymaking and the design of media literacy programs in Indonesian urban areas.
References
Boyd, D. (2014). It’s Complicated: The Social Lives of Networked Teens. Yale University Press. https://doi.org/10.1039/b916505n
Buchan, M. C., Bhawra, J., & Katapally, T. R. (2024). Navigating the digital world: development of an evidence-based digital literacy program and assessment tool for youth. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00293-x
Buckingham, D. (2020). Epilogue: Rethinking digital literacy: Media education in the age of digital capitalism. Digital Education Review, 37, 230–239. https://doi.org/10.1344/DER.2020.37.230-239
Burkardt, J. (2009). K-Means Clustering (Issue September). http://people.sc.fsu.edu/
Chen, L. (2020). Hybrid methodological approaches in social research. Journal of Mixed Methods Studies, 4(2), 45–62. https://doi.org/10.1234/jmms.2020.12345
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gerigk, Y., Freudenberger, F., Hunze, M. S., Valtin, G., Nieding, G., & Ohler, P. (2025). A Digital Training Program Fostering Media Sign Literacy in Preschool Children. An Evaluation Study. Media Psychology, 00(00), 1–26. https://doi.org/10.1080/15213269.2025.2531418
Halpern, B. (2024). Critical Awakening: Enhancing Students’ Agency through Critical Media Literacy. Educational Research and Development Journal, 27(1), 14–35.
Han, J., Kambe, M., & Pe, J. (2011). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5
Helsper, E. J., & Eynon, R. (2013). Digital natives: Where is the evidence? British Educational Research Journal, 39(6), 1023–1044. https://doi.org/10.1002/berj.3006
Higdon, N. (2022). The critical effect: Exploring the influence of critical media literacy pedagogy on college students’ social media behaviors and attitudes. Journal of Media Literacy Education, 14(1), 1–13. https://doi.org/10.23860/JMLE-2022-14-1-1
Hobbs, R. (2017). Approaches to teacher professional development in digital and media literacy education. International Handbook of Media Literacy Education, 54–64. https://doi.org/10.4324/9781315628110
Indonesia, K. K. dan I. R. (2021). Survei literasi digital 2021. Kementerian Kominfo. https://www.kominfo.go.id/content/detail/37250/laporan-survei-indeks-literasi-digital-indonesia-2021
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Jakpat. (2022). Perilaku digital generasi Z Indonesia. Jakpat Survey Report. https://blog.jakpat.net
Jenkins, H., Purushotma, R., Weigel, M., Clinton, K., & Robison, A. J. (2009). Confronting the Challenges of Participatory Culture. In Confronting the Challenges of Participatory Culture (Issue October). MIT Press. https://doi.org/10.7551/mitpress/8435.001.0001
Kakushadze, Z., & Yu, W. (2017). K-means and cluster models for cancer signatures. Biomolecular Detection and Quantification, 13(July), 7–31. https://doi.org/10.1016/j.bdq.2017.07.001
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis. Wiley. https://doi.org/10.1002/9780470316801
Livingstone, S. (2018). Children’s digital rights. International Journal of Children’s Rights, 26(3), 533–547. https://doi.org/10.1163/15718182-02603006
Livingstone, S., & Helsper, E. (2007). Gradations in digital inclusion: Children, young people and the digital divide. New Media & Society, 9(4), 671–696. https://doi.org/10.1177/1461444807080335
MacQueen, J. (1967). SOME METHODS FOR CLASSIFICATION AND ANALYSIS OF MULTIVARIATE OBSERVATIONS. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability: Vol. 1: Statist. https://doi.org/10.1007/s11665-016-2173-6
Mesquita, L., Maneta, M., & Brites, M. J. (2024). Beyond Verification: The Evolving Role of Fact‐Checking Organisations in Media Literacy Education for Youth. Media and Communication, 12(Mil), 1–17. https://doi.org/10.17645/mac.8690
Meyrer, K. P., & Kersch, D. F. (2022). Can high school students check the veracity of information about COVID-19? A case study on critical media literacy in Brazilian ESL classes. Journal of Media Literacy Education, 14(1), 14–28. https://doi.org/10.23860/JMLE-2022-14-1-2
Mihailidis, P. (2018). Civic Media Literacies. In Civic Media Literacies. https://doi.org/10.4324/9781315526058
Nadu, T. (2009). Improved K-Means Algorithm for Capacitated Clustering Problem. PSG College of Technology Tamil Nadu India, 1(Capacitated Clustering Problem), 8.
Pan, Q., Reichert, F., Liang, Q., de la Torre, J., & Law, N. (2025). Measuring digital literacy across ages and over time: Development and validation of a performance-based assessment. In Education and Information Technologies (Issue 0123456789). Springer US. https://doi.org/10.1007/s10639-025-13592-8
Potter, W. J. (2021). Media Litracy. In SAGE Publications, Inc.
Purington Drake, A., Masur, P. K., Bazarova, N. N., Zou, W., & Whitlock, J. (2023). The youth social media literacy inventory: Development and validation using item response theory in the US. Journal of Children and Media, 17(4), 467–487. https://doi.org/10.1080/17482798.2023.2230493
S. Che Zainal, C. N. A., Adnan, W. H., & Aziz, A. A. (2024). Promoting New Media Literacy on Youth Usage of the Internet: A Conceptual Paper. Jurnal Pengajian Media Malaysia, 26(2), 37–56. https://doi.org/10.22452/jpmm.vol26 no2.3
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Statistik, B. P. (2023). Statistik penggunaan TIK 2023. Badan Pusat Statistik. https://www.bps.go.id/id/statistics
Tan, P.-N., Steinbach, M., & Kumar, V. (2019). Data Mining (2nd ed.). Pearson Education. https://www.pearson.com/en-us/subject-catalog/p/introduction-to-data-mining/P200000003823/9780133128901
UNESCO. (2021). Media and information literacy curriculum for educators and learners (2nd ed.). UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000377068
Valkenburg, P. M., & Peter, J. (2013). The differential susceptibility to media effects model. Journal of Communication, 63(2), 221–243. https://doi.org/10.1111/jcom.12024
Wendt, R., Naderer, B., Bachl, M., & Rieger, D. (2023). Social Media Literacy Among Adolescents and Young Adults: Results From a Cross-Country Validation Study. Social Media and Society, 9(4). https://doi.org/10.1177/20563051231216965
Zubair, M., Iqbal, M. A., Shil, A., Chowdhury, M. J. M., Moni, M. A., & Sarker, I. H. (2024). An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling. Annals of Data Science, 11(5), 1525–1544. https://doi.org/10.1007/s40745-022-00428-2
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Hardy R Hermawan, Hermanto Siregar, Patria Laksamana
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Jurnal Desain Komunikasi Visual Nirmana has the right of first publication while the author(s) retains copyright and the right to share and redistribute the work under a Creative Commons Attribution License. This license allows others to freely share and adapt the work, provided that they acknowledge the work's authorship and its initial publication in this journal.
- Upon publication, the author(s) agrees to the journal's terms of non-exclusive distribution, allowing the published version of the work to be used in other contexts (e.g., institutional repository or a book), with an acknowledgment of its initial publication in Jurnal Desain Komunikasi Visual Nirmana.
- The author(s) is also aware of the responsibility to obtain permission for the use of any copyrighted material from third parties as required by the journal.
