IDENTIFIKASI JENIS KAYU BERBASIS CITRA MENGGUNAKAN PROBABILISTIC NEURAL NETWORK (PNN) - Ismi Amalia (Jurnal Teknologi, 2014)
The purpose of this research is to identify the types of wood based on imagery by using PNN method. In this paper, gray level co-occurence matrix (GLCM) is used as texture classification techniques. The GLCMs are generated to obtain three features: autocorrelation, cluster shade and sum variance. The classification technique used to classify the wood species is a probabilistic neural network (PNN). This research was carried out using 12 different types of wood. For each type of wood, 6 images were collected. The images of wood were divided in two sets: training set and test set. The leave-one-out cross-validation technique was applied for model validation. Our experimental results showed that the proposed method can increase the recognition rate up to 80.55%. The result of this research indicated that three features of GLCM are accurate to distinguish types of wood. This research used only a small-size dataset, so for further research is needed to use more feature extract methods and types of wood