Implementasi Algoritma Convolutional Neural Network Untuk Klasifikasi Citra Kemasan Kardus Defect dan No Defect

Abstract

Packaging is an important aspect of a product, because packaging can affect the quality and competitiveness of the product. Damaged packaging can result in decreased product quality. One popular packaging used is corrugated cardboard type box. To visually distinguish defect and no defect cardboard packaging, there are tears, holes and dents on the defect cardboard packaging. Whereas the no defect cardboard packaging has a visual that there are no tears, holes or dents. To simplify the classification, technology is needed that can distinguish between defect and no-defect cardboard packaging. In this study the total images used as a dataset are 1300 images, which are then divided into 2 with a percentage of 80% for training data and 20% for test data. The dataset first goes through the preprocessing stage before being used. Preprocessing consists of cropping, augmentation and resizing. And also do the segmentation process using Grabcut method. Then feature extraction is also performed using Local Binary Pattern (LBP). This study uses the Convolutional Neural Network algorithm with a total of 3 convolution layers, namely 16.32 and 64 and the Adam optimizer. Four experiments were carried out by differentiating the hyperparameter epoch, the input image size and the learning rate. The results showed that the model produced with an epoch hyperparameter of 30, an input image size of 300x300 and a learning rate of 0.001 obtained the best performance with an accuracy value of 95.77%, 96% precision, 96% recall, 96% f1-score and loss of 0.1478.