Indonesian Commercial Woods Classification Based on GLCM and K-Nearest Neighbor

Abstract

Currently, the presence of wood is becoming increasingly scarce. In addition, the recognition of wood is still using wood experts, who basing their judgments on the characteristics that can be seen by eye directly such as color, texture and so on. However, wood experts are still few and have a disadvantage that the results obtained are still not sufficiently accurate and time consuming. The purpose of this research is to develop Indonesian commercial woods classification system based on GLCM and k-Nearest Neighbor. Procedures of the wood classification system includes image acquisition using a digital camera, then a preprocessing steps by converting the original image to grayscale image and sharpening the image, after that do texture feature extraction using Gray Level Cooccurrence Matrix (GLCM) with the parameters used are Contrast, Correlation , Energy, Entropy, and Homogeneity, at each direction that are 0°, 45°, 90°, 135°,and the last step is the classification using the k-Nearest Neighbor (k-NN). The testing results show that the testing data can be classified accurately 100% is a testing data derived from the training database with k = 1. In general, the greater the value of k then the classification success rate decreases.