Classification of Cedar Wood Quality using Convolutional Neural Network Method
Renal Farhan, Muhammad Ary Murti, Casi Setianingsih · Zenodo (CERN European Organization for Nuclear Research) · 2021
As a fulfillment of household appliance needs, cedarwood is one of the most sought after materials [1]. Apart from its distinctive fragrant, quality is the main point of concern. The quality of this wood can be classified based on fiber patterns. In general, the wood processing industry does the classification process manually by relying on the sense of sight. As a result of the accuracy and time efficiency also varies so that it can reduce the credibility of the local wood industry. machine learning is the solution to solve that problem. Some researches have been done, one of which uses the HOG feature and SVM classification with an accuracy of 90% and a time of 1.40 seconds [2]. However, in the industrial era 4.0 which incidentally paid great attention to technological updates, so in this paper, the writer implements one method of deep learning in the classification system of cedarwood, the Convolutional Neural Network. The dataset used consists of five classes: Class A, Class B, Class C, Class D and Class E. The feature extraction process is carried out at the convolution, activation, and pooling layers. Total layers used are 16 weight layers with input in the form of images are taken automatically using the Logitech Brio 4K integrated with Arduino Uno and ultrasonic sensors. The experimental results showed a significant improvisation with an accuracy of 97% and a prediction speed of 0.56 seconds.