Performance Comparison Between LeNet And MobileNet In Convolutional Neural Network for Lampung Batik Image Identification

Abstract

Purpose: The rich cultural heritage of Indonesia includes the intricate art of batik, which varies across regions with unique patterns and motifs. This study focuses on Lampung batik, a distinctive type of batik, representing Lampung Province, Indonesia. Leveraging Convolutional Neural Network (CNN) architectures, namely LeNet-5 and MobileNet, the research compares their effectiveness in recognizing and classifying Lampung batik motifs. Data augmentation techniques, including rotation, brightness, and zoom, were employed to enhance the dataset and improve model performance.Methods: The study collected 500 Lampung batik images categorized into 10 classes which were then augmented and divided into training, validation, and testing sets. The model was created using a Deep Learning approach, LeNet And MobileNet. Both models were trained using identical hyperparameters and evaluated based on their accuracy in classifying Lampung batik motifs.Results: The results demonstrate an accuracy of 99.33% for LeNet-5 and 98.00% for MobileNet, outperforming previous studies. LeNet-5, particularly with augmentation, exhibited superior precision and recall in classifying Lampung batik motifs. This research underscores the efficacy of CNN architectures, coupled with data augmentation techniques, in accurately identifying intricate cultural artifacts like Lampung batik.Novelty: The Dharmagita learning model using a mobile application is a new model that has not existed before.