Klasifikasi Jenis Cacat pada Kulit Menggunakan Arsitektur GoogLeNet - Prananda, Alifia Revan; Frannita, Eka Legya (UNIB Press, 2024)
Deep learning has been proven to be able to provide significant contributions to several fields, including industry. It has also been proven that it has resulted in an outstanding performance for classification, detection, and even segmentation processes. In the leather industry, it also successfully gave valuable results, especially for the leather defect inspection process. This study aims to develop deep learning architecture for classifying leather defect. We used 3600 leather digital images distributed in six types of leather defects. In this study we employed GoogLeNet for classifying the data. Our experiment successfully achieved accuracy of 0.904 in training process and 0.885 in testing process. This result indicated that GoogLeNet provided powerful performance for classifying the type of leather defects.