Perbandingan Efficientnetv2 Dan Mobilenetv3 Pada Cnn Untuk Klasifikasi Gambar Asli Dan Gambar Ai

Abstrak

The rapid advancement of generative artificial intelligence, particularly Generative Adversarial Networks and Diffusion Models, has enabled the creation of synthetic images with a visual quality that is increasingly difficult to distinguish from authentic photographs, raising concerns over misinformation, media manipulation, and digital identity misuse. This study implements a Convolutional Neural Network (CNN) to classify real and AI-generated images and compares the performance of two transfer learning architectures, EfficientNetV2-B0 and MobileNetV3-Large, against a CNN trained from scratch. The dataset consists of 10,930 images collected from two Kaggle repositories, comprising 5,508 AI-generated images and 5,422 real images, which were split into 80% training, 10% validation, and 10% testing data, resized to 224x224 pixels, and augmented prior to training using a batch size of 32, a maximum of 20 epochs, a learning rate of 0.001, and the Adam optimizer. The experimental results show that MobileNetV3-Large achieved the best performance with a training accuracy of 96.84%, a validation accuracy of 96.25%, a testing accuracy of 96.71%, and a testing loss of 0.1031, outperforming EfficientNetV2-B0 (94.41% testing accuracy) and the CNN trained from scratch (91.58% testing accuracy). Hyperparameter experiments further confirm that a batch size of 32 combined with 20 training epochs produces the most stable convergence across all three architectures. The best-performing model was subsequently deployed as a REST API using the Flask framework to support real-time image classification. These findings indicate that transfer learning, particularly with the MobileNetV3-Large architecture, provides an effective and computationally efficient approach for detecting AI-generated images