Integration of Deepfake Technology in Promotional Videos to Enhance MSME Economic Utility in Bireuen Regency

Authors

  • Heri Gustami Universitas Almuslim Bireuen
  • Deni Sumantri Program Studi Ekonomi Pembangunan Fakultas Ekonomi Universitas Almuslim Bireuen-Aceh Indonesia
  • Najmuddin Najmuddin Program Studi Pendidikan Dasar Program Pascasarjana Universitas Almuslim Bireuen-Aceh Indonesia

DOI:

https://doi.org/10.30865/json.v7i3.9676

Keywords:

Deepfake, UMKM, Digital_promotion, Ethics, Reputational_risk, Economic_utility

Abstract

This study examines the integration of deepfake technology in UMKM promotional practices by focusing on its strategic value, ethical dimensions, implementation risks, and economic utility. A qualitative approach with a naturalistic phenomenological design was employed to explore the experiences, perceptions, and readiness of UMKM actors, promotion teams, and consumers in using deepfake-based promotional content. Data were collected through participatory observation, in-depth interviews, and documentation, and analyzed using the interactive model of Miles, Huberman, and Saldaña with NVivo support. The findings indicate that deepfake is perceived as a promising promotional innovation because it can enhance visual appeal, extend promotional reach, and improve cost and time efficiency in content production. However, its adoption is shaped by important constraints, particularly content authenticity, ethical considerations, audience response, human resource capacity, and the risk of misleading consumers. The study also shows that the economic utility of deepfake in UMKM promotion is meaningful only when promotional efficiency is balanced with ethical control, digital literacy, and consumer trust. These findings suggest that deepfake adoption in UMKM should not be understood merely as a technological issue, but as a negotiation between economic value, digital capability, reputational risk, and moral legitimacy.Abstract is a brief summary of the paper to help readers quickly determine the main research problem, solutions to solving problems encountered, research objectives and temporary research results which can be in the form of numbers/percentages according to research needs. Abstract should be clear and informative, providing a statement for the problem under study and its solution. Abstract length between 90 to 230 words. Avoid unusual abbreviations and define all symbols used in the abstract. Using keywords related to the research topic is recommended.

References

Lokot Muda Harahap, Austin Beyn Beril Jahran Saragih, Rizky Ramadhan, Oscar Majeovan Surbakti, and Jonatan Gerald, “Peran UMKM dalam Sistem Perekonomian Indonesia: Tantangan dan Peluang Pasca Pandemi,” J. Ilmu Manajemen, Bisnis dan Ekon., vol. 3, no. 1 SE-Articles, pp. 78–85, Mar. 2025, doi: 10.59971/jimbe.v3i1.430.

D. P. Nasution, “Keterkaitan UMKM dalam Mengurangi Kemiskinan,” Penerbit Tahta Media, 2023.

D. Andriana, J. Fadilah, and W. Widarti, “Optimalisasi Strategi Komunikasi Pemasaran UMKM dengan Teknologi AI: Implikasi dan Rekomendasi,” J. Ilmu Komun., vol. 11, no. 2, pp. 78–89, 2024.

J. Maknunah and A. Prasetyo, “Pelatihan Pembuatan Konten Pemasaran Untuk Menunjang Promosi UMKM Di Kabupaten Malang,” JMM-Jurnal Masy. Merdeka, vol. 5, no. 2, pp. 95–104, 2022.

D. Feng, X. Lu, and X. Lin, “Deep detection for face manipulation,” in Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part V 27, Springer, 2020, pp. 316–323.

P. Korshunov and S. Marcel, “Deepfakes: a new threat to face recognition? assessment and detection,” arXiv Prepr. arXiv1812.08685, 2018.

S. Liu, H. Jiang, S. Chen, J. Ye, R. He, and Z. Sun, “Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning,” Transp. Res. Part E Logist. Transp. Rev., vol. 142, p. 102070, 2020.

M. Lasisi and S. Adejumo, “Digital Ethics,” D. Baker and L. B. T.-E. of L. Ellis Librarianship, and Information Science (First Edition), Eds., Oxford: Academic Press, 2025, pp. 118–124. doi: https://doi.org/10.1016/B978-0-323-95689-5.00267-4.

M. Gerlich, “The power of virtual influencers: Impact on consumer behaviour and attitudes in the age of AI,” Adm. Sci., vol. 13, no. 8, p. 178, 2023.

O. Novera, “Analisis pengaturan hukum pidana terhadap penyalahgunaan teknologi manipulasi gambar (deepfake) dalam penyebaran konten pornografi melalui akun media sosial,” El-Faqih J. Pemikir. dan Huk. Islam, vol. 10, no. 2, pp. 460–474, 2024.

M. Lasisi and S. Adejumo, “Digital Ethics,” D. Baker, Ellis Librarianship, and L. B. T.-E. of L. Information Science (First Edition), Eds., Oxford: Academic Press, 2025, pp. 118–124. doi: https://doi.org/10.1016/B978-0-323-95689-5.00267-4.

B. Chesney and D. Citron, “Deep fakes: A looming challenge for privacy, democracy, and national security,” Calif. L. Rev., vol. 107, p. 1753, 2019.

F. A. Kusuma, V. Limantara, G. Gracia, and T. Sudimin, “Etika Periklanan pada Era Digital: Hoax dan Penipuan,” J. Locus Penelit. dan Pengabdi., vol. 3, no. 7, pp. 604–613, 2024.

B. V. H. Suryanto, “Analisis Kriminologis Tindak Pidana Penyebaran Video Porno Di Media Sosial Di Kabupaten Tuban (Studi Kasus Di Polres Tuban),” Universitas Muhammadiyah Malang, 2021.

A. N. Fitriana, V. S. Mujahida, A. Z. Abidin, A. Naufal, and M. R. Rafli, “Negative Impact of Artificial Intelligence in the World of Image-based Editing,” Interkoneksi J. Comput. Sci. Digit. Bus., vol. 2, no. 2, pp. 85–93, 2024.

A. Abdullah, A. R. Asshiddiqi, F. Arviandi, R. Isnaini, T. Meilani, and V. J. Antonia, “Pengaruh Globalisasi terhadap Budaya Indonesia serta Tantangan dalam Mempertahankan Rasa Nasionalisme,” J. Intelek Insa. Cendikia, vol. 1, no. 10, pp. 6866–6871, 2024.

M. Enshassi, R. J. Nathan, Soekmawati, and H. Ismail, “Unveiling barriers and drivers of AI adoption for digital marketing in Malaysian SMEs,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 11, no. 2, art. 100519, 2025.

J. W. Creswell and C. N. Poth, Qualitative Inquiry and Research Design: Choosing Among Five Approaches, 4th ed. Thousand Oaks, CA, USA: SAGE Publications, 2018.

Y. S. Lincoln and E. G. Guba, Naturalistic Inquiry. Newbury Park, CA, USA: SAGE Publications, 1985.

M. B. Miles, A. M. Huberman, and J. Saldaña, Qualitative Data Analysis: A Methods Sourcebook, 4th ed. Thousand Oaks, CA, USA: SAGE Publications, 2019.

B. Saunders et al., “Saturation in qualitative research: Exploring its conceptualization and operationalization,” Qual. Quant., vol. 52, pp. 1893–1907, 2018.

S. Rahimi and M. Khatooni, “Saturation in qualitative research: An evolutionary concept analysis,” SSM - Qualitative Research in Health, vol. 4, 2024.

D. B. Allsop, R. Chelladurai, and J. H. Kim, “Qualitative methods with NVivo software: A practical guide for analyzing qualitative data,” Soc. Sci., vol. 4, no. 2, 2022.

L. P. Wong, “Data analysis in qualitative research: A brief guide to using NVivo,” Malays. Fam. Physician, vol. 3, no. 1, pp. 14–20, 2008.

S. Sarosa, Analisis Data Penelitian Kualitatif. Yogyakarta, Indonesia: PT Kanisius, 2021.

M. F. Arroyabe, C. F. A. Arranz, I. Fernández de Arroyabe, and J. C. Fernández de Arroyabe, “Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment,” Technology in Society, vol. 79, art. 102733, 2024.

E. Sánchez, R. Calderón, and F. Herrera, “Artificial Intelligence Adoption in SMEs: Survey Based on TOE–DOI Framework, Primary Methodology and Challenges,” Applied Sciences, vol. 15, no. 12, art. 6465, 2025.

T. Le Dinh, M.-C. Vu, and G. T. C. Tran, “Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications,” Information, vol. 16, no. 5, art. 415, 2025.

F. ul Haq, N. M. Suki, H. Zaigham, A. Masood, and A. Rajput, “Exploring AI Adoption and SME Performance in Resource-Constrained Environments: A TOE–RBV Perspective with Mediation and Moderation Effects,” Journal of Digital Economy, 2025.

D. Khalfallah and V. Keller, “Authenticity, ethics, and transparency in virtual influencer marketing: A cross-cultural analysis of consumer trust and engagement: A systematic literature review,” Acta Psychologica, 2025.

T. H. Baek, J. Kim, and J. H. Kim, “Effect of disclosing AI-generated content on prosocial advertising evaluation,” International Journal of Advertising, 2024.

J. L. Grigsby, M. Michelsen, and C. Zamudio, “Service ads in the era of generative AI: Disclosures, trust, and intangibility,” Journal of Retailing and Consumer Services, vol. 84, art. 104231, 2025.

C. Luo, N. A. M. Hasan, and G. Lei, “Influence of short video content on consumers’ purchase intentions on social media platforms with trust as a mediator,” Scientific Reports, 2025.

X. Shen and Y. Wang, “How short video marketing influences purchase intention in social commerce: The role of users’ persona perception, shared values, and individual-level factors,” Humanities and Social Sciences Communications, 2024.

N. Hynek, B. Gavurova, and M. Kubak, “Risks and benefits of artificial intelligence deepfakes: Systematic review and comparison of public attitudes in seven European countries,” Journal of Innovation & Knowledge, vol. 10, no. 5, art. 100782, 2025.

O.-I. Moisescu, L.-G. Sterie, and D. Mican, “Fake news, consumer cynicism and negative word-of-mouth: The mitigating role of trust in social media advertising,” Computers in Human Behavior, 2025.

Y. Chen, X. Zhang, and Y. Liu, “Consumer attitudes toward AI-generated ads: Appeal types, self-efficacy and AI’s social role,” Journal of Business Research, vol. 185, 2024.

L. Zhang and C. Hur, “The Impact of Generative AI Images on Consumer Attitudes in Advertising,” Administrative Sciences, vol. 15, no. 10, art. 395, 2025.

G. Vecchietti, G. Liyanaarachchi, and G. Viglia, “Managing deepfakes with artificial intelligence: Introducing the business privacy calculus,” Journal of Business Research, vol. 186, art. 115010, 2025..

Downloads

Published

2026-03-31

How to Cite

Heri Gustami, Sumantri, D., & Najmuddin, N. (2026). Integration of Deepfake Technology in Promotional Videos to Enhance MSME Economic Utility in Bireuen Regency. Jurnal Sistem Komputer Dan Informatika (JSON), 7(3), 1196–1207. https://doi.org/10.30865/json.v7i3.9676

Issue

Section

Articles