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All Journal Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Yuliana, Siti Sarah
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Journal of Intelligent Systems Technology and Informatics

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Skin Tone Classification in Digital Images Using CNN For Make-Up and Color Recommendation Nurapipah, Nida; Yuliana, Siti Sarah
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.29

Abstract

Human skin tone variation is an obstacle in the development of a digital beauty product recommendation system. The purpose of this study is to categorize skin tone into three groups (Black, Brown, and White). Using a Convolutional Neural Network (CNN) based on the refined EfficientNetB0 architecture on a balanced dataset of 1,500 facial images, each class consisting of 500 images. All images in the dataset have been resized to 224 × 224 pixels to match the model input and ensure data uniformity and compatibility with the EfficientNetB0 model architecture used. The dataset used was obtained from the Kaggle platform and processed through the normalization and augmentation stages. It was then evaluated through the validation process using the 5-fold cross-validation method. This model achieved a total accuracy level of 88.67%, with the white category demonstrating precision (0.93), recall (0.95), and F1-score (0.94), as well as the highest AUC of 0.99, indicating very satisfactory performance. Additionally, this system can offer personalized beauty product recommendations, including foundation shades, lipstick colors, and clothing color palettes, tailored to specific skin tones. This method enhances the user experience by providing accurate recommendations that adapt to various lighting conditions, making it suitable for use on digital beauty platforms.
Co-Authors Alfarizzi, Mohamad Risky Annas, Elpan Jamaludin Azham, Moch Nazham Ismul Fadhillah, Achmad Ridwan Fadilah, Ilham Fauziah, Hany H, Jingga Fajrianti Herliani, Desi Maulana, Muhamad Andhika Mawari, Rahma Siti Mutaqin, Fahmi Hidayatul Muwafiq, Syahdan Fatan Nur Nurapipah, Nida Pamungkas, Aldi Tryana Putri, Riva Apriliyani Rahman, Roy Hadi Rifai, Muhammad Rizal RR. Ella Evrita Hestiandari Septian, Diaz Ahmad Taptajani, Dedi Sa'dudin Wijdan, Farid Muhammad