Segmentasi Citra Wajah Berdasarkan Tekstur Kulit Menggunakan Metode K-Means Clustering - Nurul Hayati; Heri Santoso; Sriani Sriani (Universitas Pahlawan Tuanku Tambusai, 2023)
The aim of this research is to develop a facial skin image segmentation system that can differentiate between normal and abnormal skin types. This system is intended to assist individuals in accurately determining their skin type, thereby avoiding mistakes in purchasing facial care products. Common problems associated with using inappropriate skincare products include facial skin inflammation, acne breakouts, dryness-induced wrinkles, and the appearance of dark spots. These issues often arise due to a lack of understanding about different skin types and their specific care requirements. To address this problem, the author plans to employ image segmentation techniques using the K-Means Clustering algorithm to analyze the distribution of texture abnormalities in the skin. By leveraging image processing technology, the author aims to provide a practical and effective solution to the problem of purchasing skincare products unsuitable for an individual's skin type. This segmentation system is expected to offer users clearer guidance in selecting facial care products tailored to their specific skin type.