Abstract—Skin disease is one of disease that is often found in
tropical countries, such as Indonesia. People who suffered from
skin disease in Indonesia were still relatively high, the
prevalence could range between 20% - 80%. Therefore, the
help of computer technology was expected to detect the disease
earlier that attacked the skin in the human’s body and it could
reduce the possibility of the occurrence for other dangerous
diseases. This study proposed the making of an application of
identification image for skin disease by using one of the
machines learning method, called Support Vector Machine
(SVM) which was done by processing the image and machine
learning processes that could perform early detection of skin
diseases. This study aimed to determine the classification of
skin diseases in humans into four classes, such as the class
Benign Keratosis, Melanoma, Nevus, and Vascular. The
segmentation method used was K-Means Clustering, while the
feature extraction method that used was feature extraction of
the Discrete Wavelet Transform (DWT) and Color Moments.
Based on the results of the test that had conducted, the
sensitivity was 95%, the specificity was 97.9% and the
accuracy was 97.1% by using SVM parameters, that was
kernel Radial Basis Function (RBF), Box Constraint = 1.5,
RBF_Sigma (σ) = 1, and iterations = 1000.
Index Terms—Skin disease, K-means clustering, discrete wavelet transform, color moments, SVM.
The authors are with the Departement of Information Technology, Faculty of Engineering, Udayana University, Bali 80361, Indonesia (e-mail: email@example.com, firstname.lastname@example.org email@example.com, firstname.lastname@example.org).
Cite: I Ketut Gede Darma Putra, Ni Putu Ayu Oka Wiastini, Kadek Suar Wibawa, and I Made Suwija Putra, "Identification of Skin Disease Using K-Means Clustering, Discrete Wavelet Transform, Color Moments and Support Vector Machine," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 542-548, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).