Lampung Batik Classification Using AlexNet, EfficientNet, LeNet and MobileNet Architecture
Rico Andrian, Rahman Taufik, Didik Kurniawan, Abbie Syeh Nahri, Hans Christian Herwanto · International Journal of Advanced Computer Science and Applications · 2024
This study explores the application of image recognition technology based on Convolutional Neural Network (CNN) to classify Lampung batik motifs. Four CNN architectures are employed, namely AlexNet, EfficientNet, LeNet, and MobileNet. The dataset consist of ten motif classes, including Siger Ratu Agung, Sembagi, Jung Agung, Kembang Cengkih, Granitan, Abstract, Sinaran, Tambal, Kambil Sicukil, and Sekar Jagat. It comprises a total of 1000 images of Lampung Batik motifs, which were enhanced using preprocessing techniques such as rotation, shifting, brightness adjustment, and zooming. The classification results show that AlexNet achieves an accuracy of 95.33%, EfficientNet achieves 98.00%, LeNet achieves 99.33%, and MobileNet achieves 98.00%. The best accuracy result was achieved by the LeNet architecture, attributed to its suitability for small datasets. While some classification errors occurred due to similarities in patterns and variations in image positions, employing more advanced methods to better distinguish between similar motifs could address these challenges. This study highlights the effectiveness of CNN architectures in supporting the recognition of Lampung Batik motifs, contributing to the understanding and preservation of Indonesia's cultural heritage.