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Personalize Fashion Design Based on Body Data and Consumer Preferences Using Artificial Intelligence Technology Ajeng Atma Kusuma; Aini Adila Rusydiana; Rizka Nur Aziza; Zahra Syifa Aulia; Nuha Nadhifah
Proceeding of the International Conferences on Engineering Sciences Vol. 3 No. 1 (2026): January : Proceeding of the International Conferences on Engineering Sciences
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/iconfes.v3i1.186

Abstract

The development of artificial intelligence technology is a great opportunity for the fashion industry, especially in designers based on personalization and consumer needs. This study aims to examine Midjourney's AI technology in the design personalization process by integrating solid data and consumer style preferences. This research is expected to support the concept of mass customization in the fashion industry and increase the relevance of design to user character. This research uses a mixed method method by combining quantitative data and qualitative data. The research stages include body data collection and style preferences, prompt formulation, data-driven prompt formulation, design generation using Midjourney, design validation by experts and consumers, and integrated data analysis.The results showed that the majority of the designs produced were considered feasible in terms of construction (83%) and in accordance with the character of the consumer's body (75%). The modest and minimalist style categories received the highest personalization scores. The qualitative findings reinforce the quantitative results, showing that consumers feel the fit of the style and proportions of the design with the character of their bodies.The study concludes that Midjourney's AI integration in the fashion design process is able to effectively support design personalization, although it still requires the role of designers in technical refinement. This approach has the potential to be an innovative solution in the development of data-driven fashion design.