Implementation of Nearest Neighbor using HSV to Identify Skin Disease
Open Access
- 25 January 2018
- journal article
- research article
- Published by IOP Publishing in IOP Conference Series: Materials Science and Engineering
- Vol. 288 (1), 012153
- https://doi.org/10.1088/1757-899x/288/1/012153
Abstract
Today, Android is one of the most widely used operating system in the world. Most of android device has a camera that could capture an image, this feature could be optimized to identify skin disease. The disease is one of health problem caused by bacterium, fungi, and virus. The symptoms of skin disease usually visible. In this work, the symptoms that captured as image contains HSV in every pixel of the image. HSV can extracted and then calculate to earn euclidean value. The value compared using nearest neighbor algorithm to discover closer value between image testing and image training to get highest value that decide class label or type of skin disease. The testing result show that 166 of 200 or about 80% is accurate. There are some reasons that influence the result of classification model like number of image training and quality of android device's camera.Keywords
This publication has 1 reference indexed in Scilit:
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