Rahillda Nadhirah Norizzaty Rahiddin, Ummi Raba’ah Hashim, Lizawati Salahuddin, Kasturi Kanchymalay, Aji Prasetya Wibawa, Teo Hong Chun
This paper evaluates timber defect classification performance across four various Local Binary Patterns (LBP). The light and heavy timber used in the study are Rubberwood, KSK, Merbau, and Meranti, and eight natural timber defects involved; bark pocket, blue stain, borer holes, brown stain, knot, rot, split, and wane. A series of LBP feature sets were created by employing the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP in a phase of feature extraction procedures. Several common classifiers were used to further separate the timber defect classes, which are Artificial Neural Network (ANN), J48 Decision Tree (J48), and K-Nearest Neighbor (KNN). Uniform LBP with ANN classifier provides the best performance at 63.4%, superior to all other LBP types. Features from Merbau provide the greatest F-measure when comparing the performance of the ANN classifier with Uniform LBP across timber fault classes and clean wood, surpassing other feature sets. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia; Department of Electrical Engineering, Engineering Faculty, Universitas Negeri Malang, Malang, Indonesia; Department of Information Technology and Communication, Politeknik Mersing, Johor, Malaysia