Identification of Types of Wood using Convolutional Neural Network with Mobilenet Architecture

Abstract

Indonesia is a wood producing with large number of forest and various type of trees in less than 4000 species of trees in Indonesia’s forest. The activity of wood identification is effort to get information about kind of wood. The identification type of wood that have similar characteristics, it is difficult to identify the right type of wood. The characteristic can be allotted to two group, general characteristic and anatomy characteristic. General characteristics can be seen directly by the senses without tools, while anatomy characteristics can be seen with tools such as loupe or microscope. Convolutional Neural Network with mobilenet architecture is a Deep Learning method that can be use identify and classifying an object. In this study, using 1000 images for 10 types of wood in each type. The images split into 90 images training dataset dan 10 images for validation datasets captured by mobilephone. Based on the result of research, the obtained level of accuracy 98% training, 93,3% testing, 28% recall, and 93% for precission. That result can be concluded that performance from this model in this research is optimal to classification the kind of wood.