Komparasi Kinerja Arsitektur MobileNetV2 dan EfficientNetB0 Untuk Klasifikasi Penyakit Daun Tanaman Kedelai - Lisdiawati Lisdiawati; Siti Mutmainah; Khairunnas Khairunnas (Building of Informatics, Technology and Science (BITS), 2026)
Soybean leaf diseases can reduce the quality and productivity of plants, so an accurate and efficient detection method is needed. This study aims to compare the performance of the MobileNetV2 and EfficientNetB0 architectures in classifying soybean leaf diseases using a deep learning-based transfer learning approach. The dataset used consists of soybean leaf images grouped into several disease classes, then divided into training (80%), validation (10%), and testing (10%) data. The pre-processing stage includes resizing the images to 224 × 224 pixels, normalizing pixel values, and data augmentation in the form of rotation, shifting, zooming, and horizontal flipping. The training process is carried out using the Adam optimizer with a learning rate and applying Early Stopping to reduce the risk of overfitting. Model evaluation is carried out using a confusion matrix, accuracy, precision, recall, and F1-score. The results show that MobileNetV2 obtains an accuracy of 81%, higher than EfficientNetB0 which obtains an accuracy of 70%. The contribution of this study is to provide a comparative analysis of the effectiveness of both architectures in classifying soybean leaf diseases and to show that MobileNetV2 is more optimal for application to the dataset used.