Classification of Types of Dental Disease Using Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Methods

Abstract

Teeth are one of the organs of the human body which are quite sensitive, the many types of diseases that often occur in teeth certainly make it difficult to identify the type of disease in the teeth. This study uses 4 types of dental disease which will be classified using the Principal Component Analysis and K-Nearest Neighbor methods. For each dental disease, 10 image data were taken, with a total of 24 training data and 16 test data, a total of 40 images. The feature extraction process in this research uses RGB, HSV, and Area characteristics, for the training and testing process uses the PCA algorithm and classification uses KNN. By testing using K=1, it produces an accuracy value of 87.5% in the process of classifying types of dental disease.