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

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