INTEGRASI GEMINI 2.5 FLASH API PADA FRAMEWORK NEXT.JS UNTUK OTOMASI TRANSFORMASI VISUAL PRODUK E-COMMERCE
Abstract
Kualitas visual produk sangat menentukan konversi penjualan e-commerce, namun UMKM sering terkendala keterbatasan alat fotografi. Penelitian ini bertujuan mengimplementasikan sistem otomasi transformasi visual menggunakan Gemini 2.5 Flash API dan framework Next.js untuk menjamin performa serta keamanan sisi server. Metode penelitian mencakup inisiasi lingkungan, perancangan alur data, pemrosesan multimodal, dan validasi output. Hasilnya menunjukkan sistem berhasil mengubah foto mentah menjadi aset profesional melalui strategi Context-Aware Prompting dengan tetap menjaga integritas branding. Evaluasi teknis mencatat rata-rata waktu pemrosesan 2,9 detik, memberikan efisiensi waktu hingga 99% dibandingkan penyuntingan manual. Simpulannya, integrasi AI generatif pada platform web mampu mendemokratisasi akses branding berkualitas tinggi bagi pelaku usaha kecil guna meningkatkan daya saing digital.
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References
K. Israfilzade, “The Role of Generative Artificial Intelligence in E-Commerce : Trends , Challenges and Opportunities,” vol. 42, no. 2014, pp. 1–20, 2025.
K. Amaliah, E. W. Kenali, T. K. Khoerunnisa, D. Rofianto, H. Fathoni, and M. Fitri, “Peningkatan Daya Saing UMKM Binaan Polinela melalui Pemanfaatan Teknologi Artificial Intelligence (AI),” vol. 6, no. 2, pp. 95–106, 2024.
H. Musdalifah, F. S., Murti, K., Rahmawati, A., Aryansah, J. E., & Yogsunandar, “Product photography assistance for micro, small, and medium enterprises in Banyuasin, South Sumatra,” vol. 7, no. 10, pp. 1740–1749, 2022.
F. Ahmed and N. K. Jha, “Design and Development of a Localized E- Commerce Solution for Students focussing on Economical Sharing,” 2024.
I. Islam, R., & Ahmed, “Gemini-the most powerful LLM: Myth or Truth,” 2024 5th Inf. Commun. Technol. Conf., pp. 303–308, 2024.
A. Baranes and R. Marchant, Expanding the Generative AI Design Space through Structured Prompting and Multimodal Interfaces, vol. 1, no. 1. Association for Computing Machinery.
G. Team, “Gemini 2 . 5 : Pushing the Frontier with Advanced Reasoning , Multimodality , Long Context , and Next Generation Agentic,” pp. 1–73, 2025.
M. T. Febriyantoro, D. Arisandi, M. T. Febriyantoro, D. Arisandi, U. Universal, and M. Pemasaran, “Pemanfaatan Digital Marketing Bagi Usaha Mikro, Kecil Dan Menengah Pada Era Masyarakat Ekonomi Asean,” vol. 1, no. 2, pp. 61–76.
W. L. Hardilawati, “Strategi Bertahan UMKM di Tengah Pandemi Covid-19,” vol. 10, no. 1, 2020.
Y. Cao et al., “A Comprehensive Survey of AI-Generated Content ( AIGC ): A History of Generative AI from GAN to ChatGPT,” J. ACM, vol. 37, no. 4, 2018.
A. Haleem, M. Javaid, and R. Pratap, “An era of ChatGPT as a significant futuristic support tool : A study on features , abilities , and challenges,” BenchCouncil Trans. Benchmarks, Stand. Eval., vol. 2, no. 4, p. 100089, 2023, doi: 10.1016/j.tbench.2023.100089.
R. A. Fahnuri et al., “PEMANFAATAN ARTIFICIAL INTELLIGENCE ( AI ) DALAM PENGEMBANGAN KONTEN MULTIMEDIA DIGITAL,” vol. 4, no. 2, 2026.
G. Team, “Gemini 1 . 5 : Unlocking multimodal understanding across millions of tokens of context,” pp. 1–154, 2024.
L. Zhang, A. Rao, and M. Agrawala, “Adding Conditional Control to Text-to-Image Diffusion Models”.
P. Liu and U. States, “Pre-train , Prompt , and Predict : A Systematic Survey of Prompting Methods in Natural Language Processing Pre-train , Prompt , and Predict : A Systematic Survey,” vol. 55, no. 9, 2026, doi: 10.1145/3560815.
B. Hutchinson, V. Prabhakaran, E. Denton, K. Webster, Y. Zhong, and S. Denuyl, “Social Biases in NLP Models as Barriers for Persons with Disabilities,” pp. 5491–5501, 2020.
Y. Li, “Performance Analysis of Deep Learning Inference via Cloud-Based API Services,” IEEE Int. Conf. Web Serv., 2022.
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