Implementing The Use of AI for Analysis and Prediction in the Fashion Industry

Dr. Luri Renaningtyas (1), Dr. Putri Dwitasari (2), Dr. Nugrahardi Ramadhani (3)
(1) Head of Fashion Laboratory at Fashion Design and Textile Program, Visual Communication Design Department, Petra Christian University, Surabaya, Indonesia, Indonesia,
(2) Visual Communication Design Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia,
(3) Visual Communication Design Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia

Abstract

The COVID-19 pandemic has made all aspects of human life assisted by technology and big data. It starts from the education sector, economy, communication, health, and manufacturing to fashion. As we all know fast fashion has become one of the most significant contributors of waste.  During the flow of developing a collection, for example; the production and distribution process can cause ethical issues and contradict sustainability matters. Several studies from 2010 to date have initiated AI (Artificial Intelligent) technology, a computer vision that alleviates the use of carbon footprints in the fashion industry. AI presents robust evidence to the audience, since it is visual and statically calculated, furthermore it is less costly and energy saving. AI abstracts the similarities or differences across all clothing and collections from the dataset. Its implementation can be used in many fashion careers with different purposes. By reviewing across the computer vision journals complemented with fashion management literatures, this article eventually provides insights of the implementation of AI for analysis and prediction from fashion photos or dataset.

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Authors

Dr. Luri Renaningtyas
[email protected] (Primary Contact)
Dr. Putri Dwitasari
Dr. Nugrahardi Ramadhani
Renaningtyas, L., Dwitasari, P., & Ramadhani, N. (2023). Implementing The Use of AI for Analysis and Prediction in the Fashion Industry. The Academic Research Community Publication, 7(1). https://doi.org/10.21625/archive.v7i1.928

Article Details

Received 2022-11-28
Accepted 2023-01-30
Published 2023-01-30