Klasifikasi Penentuan Kualitas Kayu Jati Berdasarkan Citra Digital Menggunakan Algoritma K-Nearest Neighbour - Kamaruddin, Kamaruddin; Umar, Najirah; Wahyuningsih, Pujianti; Sudarsono, Firman (Universitas Wahid Hasyim, 2024)
The increase in demand for goods made from wood cannot be limited, especially the demand for table furniture, cupboards and so on. Along with development, teak wood production has shifted to superior types of teak wood because the growth period is faster, but this condition means that the quality of teak wood is not like the old type of teak wood. Difficulty in seeing the quality of wood is a problem faced by craftsmen and furniture makers. The aim of this research is to determine the quality of wood species which are divided into 3 class categories, namely class A, class B and class C. To produce a classification of wood quality, researchers use the KNN method by carrying out HSV color segmentation then analyzing the color value of each image pixel based on the tolerance value on HSV color dimensions. The results of this research were using 65 teak wood training data for each class. Testing was carried out using 27 teak wood test data with an accuracy level of 85.19%, precision reaching 85.46%, recall reaching 85.18% and F1 score reaching 85.3%.