Indonesian Commercial Woods Classification Based on GLCM and K-Nearest Neighbor
Hery Herawan1, Karmilasari2

1Hery Herawan, Department of Agrotechnology, Faculty of Industrial Technology, Gunadarma University, Depok, West Java, Indonesia. 
2Karmilasari, Department of Information System, Faculty of Computer Science & Information Technology, Gunadarma University, Depok, West Java, Indonesia.
Manuscript received on 23 July 2022 | Revised Manuscript received on 29 July 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 108-114 | Volume-11 Issue-6, August 2022 | Retrieval Number: 100.1/ijeat.F37430811622 | DOI: 10.35940/ijeat.F3743.0811622
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

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. 
Keywords: GLCM, Indonesian Commercial Woods, k-Nearest Neighbor, Wood Classification System
Scope of the Article: Classification