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All Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Internet of Things and Artificial Intelligence Journal International Journal of Research in Mathematics Education
Rozaq, Hasri Akbar Awal
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Public Health

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Accurate Skin Tone Classification for Foundation Shade Matching using GLCM Features-K-Nearest Neighbor Algorithm Syahputra, Muhammad Reza; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Sutaji, Deni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4723

Abstract

Foundation shade matching remains a significant challenge in the beauty industry, particularly in Indonesia where consumers exhibit three distinct skin tone categories: ivory white, amber yellow, and tan. Manual foundation selection often results in mismatched shades, leading to customer dissatisfaction. This study presents a novel automated skin tone classification system combining Gray Level Co-Occurrence Matrix (GLCM) feature extraction with the K-Nearest Neighbor (KNN) algorithm. The GLCM method extracts four key texture features (contrast, homogeneity, energy, and entropy) from facial images, while KNN performs classification. A comprehensive dataset of 963 facial images was used, with 770 training and 193 test samples collected under controlled lighting conditions. After testing K values from 1 to 15, the optimal K=1 achieved 75.65% accuracy. Compared to baseline color histogram methods (60% accuracy), our GLCM-KNN approach demonstrates 15.65% improvement in classification performance. This research contributes to computer vision applications in beauty technology, enabling the development of mobile applications for virtual foundation try-on and personalized product recommendations. The findings have significant implications for the cosmetics industry, particularly for automated cosmetic shade matching systems and enhanced customer experience in online beauty retail. Further research is recommended to explore deep learning approaches and expand dataset diversity to improve accuracy.
Co-Authors Abdilah, Muhammad Fariz Fata Al Ghifari, Muhammad Akmal Ananda, Rona Sepri Andi Farmadi Anshari, Muhammad Ridha Dani Arifudin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Erdi, Muhammad Fatatik, Isna Nurul Fatma Indriani Imam Tahyudin Irwan Budiman Mahmud Mahmud Maulida, Trisna Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Mutmainah, Fatikhatun Prihasno, Izlah Nur Fadlila Herawati Putra, Feishal Azriel Arya Radityo Adi Nugroho Rudy Herteno Saputro, Setyo Wahyu Saragih, Triando Hamonangan Syafaat, Alif Yahya Syahputra, Muhammad Reza Wahyu Caesarendra Wardana, Muhammad Difha YILDIZ, Oktay Zuhdi, Shaifudin Zumaroh, Agnis Nur Afa