Konstruksi Makna Visual dalam Iklan Fashion: Perbandingan antara Fotografi AI Generatif dan Fotografi Konvensional

Dedit priyono; Irfa  Rifaah

doi:10.32664/mavis.v7i02.2102

Penulis

Dedit priyono Politeknik STTT Bandung
Irfa Rifaah Politeknik STTT Bandung

DOI:

https://doi.org/10.32664/mavis.v7i02.2102

Kata Kunci:

AI Generatif, Fotografi Fashion, Komunikasi Visual, Persepsi Audiens, Semiotika.

Abstrak

Kemajuan teknologi kecerdasan buatan (AI) generatif telah mengubah lanskap produksi visual dalam industri fashion, terutama dalam konteks fotografi iklan. Studi ini bertujuan untuk menganalisis konstruksi makna visual dan persepsi audiens terhadap citra iklan fashion yang dihasilkan oleh AI generatif dibandingkan dengan fotografi konvensional. Studi ini menggunakan pendekatan campuran dengan metode analisis semiotik visual dan eksperimen survei berbasis skala Likert. Hasil menunjukkan bahwa gambar berbasis AI menampilkan estetika visual tinggi namun menghadapi tantangan dalam aspek otentisitas dan kredibilitas merek. Studi ini memberikan kontribusi terhadap pemahaman visual culture dalam era post-fotografi dan menawarkan wacana kritis dalam bidang komunikasi visual fashion.

Referensi

[1] J. Williamson, Decoding Advertisements: Ideology and Meaning in Advertising. New York: Marion Boyars, 1978.

[2] R. V. Kozinets and J. M. Handelman, “Adversaries of Consumption: Consumer Movements, Activism, and Ideology,” Journal of Consumer Research, vol. 31, no. 3, pp. 691–704, Dec. 2004, doi: 10.1086/425104.

[3] M. Barnard, Fashion Theory. Routledge, 2020. doi: 10.4324/9781315099620.

[4] G. Kress and T. van Leeuwen, Reading Images. Third edition. | London; New York: Routledge, 2021.: Routledge, 2020. doi: 10.4324/9781003099857.

[5] P. M. Lester, Visual Communication: Images with Messages (6th ed.). Boston: Wadsworth/Cengage Learning, 2014. [Online]. Available: https://archive.org/details/visualcommunicat0006lest/page/n3/mode/2up

[6] I. J. Goodfellow and others, “Generative Adversarial Nets,” in Advances in Neural Information Processing Systems, 2014. doi: 10.48550/arXiv.1406.2661.

[7] M. Krishna Pasupuleti, “Fashion Innovation 5.0: Transforming Design with AI, AR, VR, and 3D Printing,” in Tech-Driven Fashion Industry: Revolutionizing Design with AI, AR, VR, and 3D Printing, vol. 4, no. 11, National Education Services, 2024, pp. 135–148. doi: 10.62311/nesx/66258.

[8] H. S. Lee and M. Luo, “Transforming Creative Process: A Systematic Literature Review of Discourse on AI Image Generators,” Proceedings of the Association for Information Science and Technology, vol. 61, no. 1, pp. 986–988, Oct. 2024, doi: 10.1002/pra2.1162.

[9] P. W. Harsanto and J. W. Jakti, “Post-Photography: The Disruption Effect of Artificial Intelligence on Photography for Product Advertising,” Information Sciences Letters, vol. 12, no. 9, pp. 2141–2151, Sep. 2023, doi: 10.18576/isl/120920.

[10] W. Liu and H.-G. Kim, “The visual communication using generative artificial intelligence in the context of new media,” Sci Rep, vol. 15, no. 1, p. 11577, Apr. 2025, doi: 10.1038/s41598-025-96869-9.

[11] J. Bieńkowska, “The Effects of Artificial Intelligence on the Fashion Industry—Opportunities and Challenges for Sustainable Transformation,” Sustainable Development, vol. 33, no. 3, pp. 3774–3790, Jun. 2025, doi: 10.1002/sd.3312.

[12] X. Jiang, Z. Wu, and F. Yu, “Constructing Consumer Trust through Artificial Intelligence Generated Content,” Academic Journal of Business & Management, vol. 6, no. 8, pp. 263–272, 2024, doi: 10.25236/AJBM.2024.060839.

[13] Y. Zhang and R. Gosline, “Human favoritism, not AI aversion: People’s perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation,” Judgm Decis Mak, vol. 18, no. 41, p. e41, Nov. 2023, doi: 10.1017/jdm.2023.37.

[14] K. F. MacDorman and N. Kageki, “The Uncanny Valley: The Original Essay by Masahiro Mori,” IEEE Spectrum Robotics & Automation Magazine, vol. 19, no. 2, pp. 98–100, 2012, [Online]. Available: https://web.ics.purdue.edu/~drkelly/MoriTheUncannyValley1970.pdf

[15] J. Elkins, Visual Studies. Routledge, 2013. doi: 10.4324/9780203700587.

[16] T. C. Marvellianto, S. Sarjono, and A. Z. Ramadhan, “Perancangan Buku Fotografi Fashion Batik Jawa Timur Sebagai Upaya Meningkatkan Citra Budaya,” MAVIS : Jurnal Desain Komunikasi Visual, vol. 2, no. 01, pp. 1–7, Aug. 2020, doi: 10.32664/mavis.v2i01.476.

[17] A. P. Herwanto, “Jejak Perubahan dan Dinamika Kota Malang : Studi Literatur dalam Fotografi,” MAVIS : Jurnal Desain Komunikasi Visual, vol. 6, no. 02, pp. 67–73, Sep. 2024, doi: 10.32664/mavis.v6i02.1180.

[18] A. Field, Discovering Statistics Using IBM SPSS Statistics (6th ed.). Sage Publications, 2024.

[19] J. W. Creswell and J. D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications, 2018.

[20] R. Barthes, Image-Music-Text. Great Britain: Fontana Press, 1977.

[21] J. F. Hair and others, Multivariate Data Analysis. Pearson (Cengage Learning), 2019.

[22] J. Pallant, SPSS Survival Manual. Routledge, 2020. doi: 10.4324/9781003117452.

[23] S. L. Lohr, Sampling. Chapman and Hall/CRC, 2019. doi: 10.1201/9780429296284.

[24] K. L. Ford, T. Albritton, T. A. Dunn, K. Crawford, J. Neuwirth, and S. Bull, “Youth Study Recruitment Using Paid Advertising on Instagram, Snapchat, and Facebook: Cross-Sectional Survey Study,” JMIR Public Health Surveill, vol. 5, no. 4, p. e14080, Oct. 2019, doi: 10.2196/14080.

[25] P. S. Levy and S. Lemeshow, Sampling of Populations. Wiley, 2008. doi: 10.1002/9780470374597.

[26] C. Gkonou and E. R. Miller, “Caring and emotional labour: Language teachers’ engagement with anxious learners in private language school classrooms,” Language Teaching Research, vol. 23, no. 3, pp. 372–387, May 2019, doi: 10.1177/1362168817728739.

[27] J. Baudrillard, Simulations. America: Semiotext(e), Foreign Agents Series, 1983.