Comparing the Production Process and Results of the Visual Radio Program Suara dari Tanah Jauh: Conventional vs. AI-Based Versions
DOI:
https://doi.org/10.46961/mediasi.v7i1.1810Keywords:
Artificial Intelligence, Comparative Study, Creativity, Media Production, Visual RadioAbstract
The integration of Artificial Intelligence (AI) in broadcast media production has brought about significant changes, both in technical aspects and in work structures and creative practices. The visual radio program entitled “Voices from Afar” was chosen as the object of this study because it involves two different approaches: conventional production that relies on human involvement and AI-based production that uses automated systems. This study aims to analyze the differences in the production processes and characteristics of the results of these two models, with an emphasis on work efficiency, technical quality, and aesthetic and narrative dimensions. The method applied is descriptive-comparative qualitative through observation of the production process, in-depth interviews, and content analysis. The results show that AI-based production excels in terms of speed and technical consistency, while conventional production is stronger in narrative depth, emotional expression, and visual authenticity. These findings confirm that AI serves as a creative tool that accelerates the production process; however, AI cannot yet fully replace the human function of creating meaning. Therefore, collaboration between humans and AI is considered an important strategy for the development of visual radio production in the digital media era.
References
Arikunto, Suharsimi. (2010). Prosedur Penelitian: Suatu Pendekatan Praktik (Edisi Revisi). Jakarta: Rineka Cipta.
Beckett, C. (2019). New powers, new responsibilities: A global survey of journalism and AI. London School of Economics and Political Science (LSE).
Boden, M. A. (2016). AI: Its nature and future. Oxford University Press.
Carlson, M. (2015). The robotic reporter: Automated journalism and the redefinition of labor, compositional forms, and journalistic authority. Digital Journalism, 3(3), 416–431. https://doi.org/10.1080/21670811.2014.976412
Chesney, R., & Citron, D. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753–1819.
Diakopoulos, N. (2019). Automating the news: How algorithms are rewriting the media. Harvard University Press.
Dörr, K. N. (2016). Mapping the field of algorithmic journalism. Digital Journalism, 4(6), 700–722. https://doi.org/10.1080/21670811.2015.1096748
European Broadcasting Union (EBU). (2023). AI in public service media: Opportunities, use cases, and risks. Geneva: EBU.
Graefe, A. (2016). Guide to automated journalism. Tow Center for Digital Journalism, Columbia University.
Jenkins, H. (2006). Convergence culture: Where old and new media collide. New York University Press.
Kietzmann, J., Lee, L. W., McCarthy, I. P., & Kietzmann, T. C. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135–146. https://doi.org/10.1016/j.bushor.2019.11.006
Kong, J., Kim, J., & Bae, J. (2020). HiFi-GAN: Generative adversarial networks for efficient and high-fidelity speech synthesis. Advances in Neural Information Processing Systems (NeurIPS), 33, 17022–17033.
McLuhan, M. (1964). Understanding media: The extensions of man. McGraw-Hill.
Moleong, L. J. (2019). Metodologi penelitian kualitatif (Edisi revisi). Bandung: PT Remaja Rosdakarya.
Montal, T., & Reich, Z. (2017). I, Robot. You, Journalist: Who is the “author”? Digital Journalism, 5(7), 829–849. https://doi.org/10.1080/21670811.2016.1209083
Newman, N. (2024). Journalism, media, and technology trends and predictions 2024. Reuters Institute for the Study of Journalism.
Pavlik, J. V., & Bridges, F. (2022). The future of news: Artificial intelligence and immersive media. Routledge.
Pavlik, J. V. (2013). Innovation and the future of journalism. Digital Journalism, 1(2), 181–193.
Ren, Y., Ruan, Y., et al. (2019). FastSpeech: Fast, robust and controllable text to speech. Advances in Neural Information Processing Systems (NeurIPS), 32, 3171–3180.
Singer, U., Polyak, A., Hayes, T., & Gafni, O. (2022). Make-A-Video: Text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792.
Sugiyono. (2012). Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung: Penerbit Alfabeta.
Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human–AI interaction. Journal of Computer-Mediated Communication, 25(1), 74–88. https://doi.org/10.1093/jcmc/zmz026
Thurman, N., Dörr, K. N., & Kunert, J. (2017). When reporters get hands-on with robo-writing: Users’ perceptions of automated content. Digital Journalism, 5(10), 1230–1250. https://doi.org/10.1080/21670811.2017.1289819
Utomo, B., Ismed, M., & Satyahadi, A. (2025). The Use of Motion Graphics to Increase The Appeal Of Visual Radio Broadcasts. MEDIASI Jurnal Kajian Dan Terapan Media, Bahasa, Komunikasi, 6(2), 213–221. https://doi.org/10.46961/mediasi.v6i2.1638
Utterback, A. H. (2015). Studio television production and directing (7th Ed.). Routledge.
Vaccari, C., & Chadwick, A. (2020). Deepfakes and disinformation: Exploring the impact on trust in news. Social Media + Society, 6(1), 1–13. https://doi.org/10.1177/2056305120903408
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2026 Mohammad Ismed

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.












