Klasifikasi Image Jenis Kayu pada Furnitur dengan Convolutional Neural Network
DOI:
https://doi.org/10.61769/telematika.v18i2.617Keywords:
wood, classification, Convolutional Neural Network, image classification, confusion matrixAbstract
Different types of wood have unique patterns and colors. A classification process is necessary to identify the type of wood using the Convolutional Neural Network (CNN) method. This method enables feature extraction, which becomes the data used to classify wood species. The wood image data collected from augmentation data consists of 120 images, including teak, mahogany, oak, and pine wood types. The four wood species classes have a 70% training data and a 30% test data ratio. Each class uses four convolutional layers with filters of 32, 32, 64, and 64, and a pool size of 2x2 with 512 neurons in the hidden layer. Testing the image classification website using the confusion matrix method resulted in an accuracy of 80.5%.
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