Komparasi Kinerja ResNet50 dan MobileNetV2 pada Klasifikasi Citra Multi-Domain - Herlawati, Herlawati; Hendharsetiawan, Andy Achmad; Priatna, Wowon; Dzulqiyana, Afina Putri; Rahmadanti, Regita Ari (Jurnal Komtika (Komputasi dan Informatika), 2026)
This study aims to compare the performance of the Convolutional Neural Network (CNN) architectures ResNet50 and MobileNetV2 in multi-domain image classification, namely herbal leaf images and facial skin undertone images. The herbal plant dataset consists of 11 classes of leaf images, while the skin undertone dataset includes warm, cool, and neutral categories obtained from secondary and primary data sources. The research stages include image preprocessing, model training using transfer learning, and performance evaluation using accuracy, precision, recall, F1-score, and confusion matrix. The results show that ResNet50 achieved better performance than MobileNetV2 in both classification domains. In herbal plant classification, ResNet50 achieved an accuracy of 95.00%, while MobileNetV2 obtained 94.09%. In facial skin undertone classification, ResNet50 achieved an accuracy of 89.3% and a weighted F1-score of 0.893, whereas MobileNetV2 achieved an accuracy of 68.3% and a weighted F1-score of 0.684. These findings indicate that ResNet50 is more effective and stable than MobileNetV2 for multi-domain image classification.