Analisis Performa Transfer Learning Menggunakan MobileNetV2 untuk Klasifikasi Citra X-Ray Paru-Paru - M Choirul Amri; Lailan Sofinah Harahap; Abdul Rasyid (CV. KAMPUS AKADEMIK PUBLISING, 2026)
Pneumonia is a lung disease that requires early detection to prevent serious complications. Chest X-ray images are widely used for diagnosis; however, their interpretation still depends on medical experts. This study aims to analyze the performance of transfer learning using MobileNetV2 for classifying chest X-ray images. The Chest X-Ray Pneumonia dataset from Kaggle was used and divided into 75% training data, 15% validation data, and 10% testing data. Image preprocessing included resizing, pixel normalization, and data augmentation. The model was trained for 20 epochs using the Adam optimizer. Experimental results achieved an accuracy of 95.40%, precision of 95.62%, recall of 95.40%, and an F1-score of 95.46%. These results indicate that MobileNetV2 provides effective and stable performance for chest X-ray image.