Modeling of women shoes sizing system based on 3D foot scanner result using machine learning approach

Jamila Jamila (Politeknik ATK Yogyakarta)
Eka Legya Frannita (Politeknik ATK Yogyakarta)
Gilang Fatikhul Burhan (Politeknik ATK Yogyakarta)
Windra Bangun Nuswantoro (Politeknik ATK Yogyakarta)
Anwar Hidayat (Politeknik ATK Yogyakarta)
Erlita Pramitaningrum (Politeknik ATK Yogyakarta)
Totok Yulaidin (Universitas Bina Sehat PPNI Mojokerto)

Industrial Innovation
Vol. 2 No. 2 (2025): Industrial Innovation

Accurate shoe sizing plays a crucial role in ensuring comfort, performance, and consumer satisfaction, particularly for women whose foot shapes exhibit considerable anatomical variability. To address this challenge, this research proposes a data-driven modeling framework for developing a women’s shoe sizing system based on three-dimensional foot scanner data. The study was carried out through a systematic process consisting of data preprocessing, clustering using the K-Means algorithm, and evaluation of the clustering performance. The clustering analysis identified four optimal clusters within the dataset, representing distinct patterns in foot dimension measurements. The evaluation result, with a Silhouette Score of 0.25, indicates a moderate yet acceptable level of cohesion and separation among the clusters. These findings demonstrate that the proposed model can effectively capture the underlying structure of women’s foot morphology, providing a scientific foundation for establishing more accurate, customized, and ergonomically appropriate shoe sizing standards.