K-Nearest Neighbour Classification and Feature Extraction GLCM for Identification of Terry's Nail

Nails have a function or role that is very important to protect the soft fingertips and have a lot of nerves. In the medical world, several expert systems have begun to be used in helping doctors to diagnose a disease. This research was to detect abnormalities of the nails, Terry's nail. The textural characteristics are processed with grey level co-occurrence matrix (GLCM) and classifying method using KNN. The dataset in this study is taken from Google and also some of the paper that discusses the nail abnormalities. Nail pictures obtained are different from any source. Therefore, the image should be cut just one finger. Because when detecting terry's nail, the disorder usually occurs in all the nails. So we can use one finger. The photos of a nail that has been doing the extraction characteristics using GLCM then will be done using KNN classification. In this case the class will be divided into two classes, healthy and Terry's. From the experiment that have been done, the best accuracy results are 70.93% with 60-40 partition of dataset, K=1, light intensity values of 100-500 lux, a distance of 15 cm and an angle of 0°.