Application of Deep Learning Using Convolutional Neural Network (CNN) Method for Women’s Skin Classification - Anton, Anton; Nissa, Novia Farhan; Janiati, Angelia; Cahya, Nilam; Astuti, Puji (Universitas Negeri Semarang, 2021)
Purpose: Facial skin is the skin that protects the inner part of the face such as the eyes, nose, mouth, and other. The skin of the face consists of some type, among others, normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is hard to get to know and distinguish the type of peel, this is what causes some women’s, it is hard to determine which product cosmetology and proper care for her skin type. Methods: In this study, the method of the Convolutional Neural Network (CNN) is an appropriate method to classify the type of the skin of women of age 20 – 30 years by following a few stages using Python 3.5 with a depth of three layers. In this study, the method used CNN to distinguish the type of skin of the label object of the type of skin that a normal skin type, oily, dry and combination. A combination skin type is composed of normal and dry skin types. Result: The process of learning network CNN to get the results of the value by 67%. As for the classification of Normal skin 100%, the type of the skin of the face 100% Dry, kind of Oily facial skin 100% and combination skin type (Normal and Dry) to 100%. Novelty: It can be concluded that the use of the method of CNN in automatic object recognition in distinguishing the type of leather as a material consideration in determining the object of the image. And the classification method using CNN with the Python program to be able to classify well.