ANALISIS PERBANDINGAN KINERJA ARSITEKTUR RESNET50 DAN EFFICIENTNETB1 MENGGUNAKAN METODE FINE-TUNING UNTUK KLASIFIKASI PNEUMONIA - Daiyan Akbar Setiyadi; Sindhu Rakasiwi (LPPM Universitas Abdurrab, 2026)
Early detection of pneumonia through chest X-ray images is a crucial step in medical treatment but is often hampered by class imbalance issues in datasets, leading to biased deep learning models. This research aims to conduct a holistic performance evaluation of two modern Convolutional Neural Network (CNN) architectures, ResNet50 and EfficientNetB1, to determine the optimal model under imbalanced data conditions. The methodology employed is transfer learning with an optimized two-phase fine-tuning protocol, supported by data augmentation techniques and class_weight strategies to address data imbalance. Evaluation was performed on the public