Implementation of Nearest Neighbor using HSV to Identify Skin Disease

2nd Annual Applied Science and Engineering Conference

工业技术;自然科学

Gerhana, Y.A.^1 ; Zulfikar, W.B.^1 ; Ramdani, A.H.^1 ; Ramdhani, M.A.^1

Department of Informatics, UIN Sunan Gunung Djati, Jl. A.H. Nasution No.105, Bandung, Indonesia^1

关键词: Android devices;    Class labels;    Classification models;    Euclidean;    Image training;    Nearest neighbor algorithm;    Nearest neighbors;    Skin disease;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/288/1/012153/pdf
DOI  :  10.1088/1757-899X/288/1/012153

来源: IOP

【 摘 要 】

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.