Identifikasi jenis batik menggunakan metode Convolutional Neural Network (CNN) berbasis website - Utama, Ega Fitri Yudha Satria; Edi, Sri Winarso Martyas (AITI, 2025)
This research aims to develop a website-based system utilizing the Convolutional Neural Network (CNN) method with the MobileNetV2 model architecture to classify types of batik. The dataset used consists of 1000 batik images, covering five different types of batik, which are then segmented into groups for training, validation, and testing data. The research results show an accuracy of 92 percent, indicating the model's ability to recognize batik types with a high level of accuracy. This research utilizes technology as a tool to preserve and introduce Indonesian culture to the younger generation and the general public in a more interactive and accessible way. By implementing it through a website, knowledge about various types of batik can be expanded, and batik cultural heritage can be promoted more effectively.