Teakwood Grade Identification with GLCM and K-NN with Adaboost Optimization
(Case Study at KPH Cepu)
Abstract
Teak is one type of tree that has many functions and uses. Teak wood has a very high quality to be used as raw material for the manufacture of home furniture such as tables, chairs, cabinets, and others. But middle testers (Perhutani staff) who test the quality of wood grade have limitations if the classification uses the five senses of sight and also there are still many furniture entrepreneurs who are often mistaken about teak wood quality assessment. This resulted in a lack of quality grade teak wood used as raw material for making home appliances or for furniture and trade needs under the Perhutani Corporation, especially the Cepu Kph. The teak wood image data is then acquired through preprocessing data ready to be processed. By using GLCM as an image feature extraction both training data and testing data. After the image characteristics are obtained, the image is classified by the K-Nearest Neighbor method with adaboost optimization. The final result is obtained in the form of wood grade quality classification namely grade A, B, C and D according to the class
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