Perbandingan Kinerja MobileNetV2 dan VGG16 dalam Klasifikasi Penyakit pada Citra Daun Tanaman Cabai - Itsnaini Irvina Khoirunnisa; Abdul Fadlil; Herman Yuliansyah (JISKA (Jurnal Informatika Sunan Kalijaga), 2026)
Chili peppers play a crucial role in the Indonesian economy, serving as a significant source of income for many farmers. Price fluctuations influenced by weather conditions make this crop vulnerable to diseases that can impact productivity. However, leaves are key indicators of plant health, revealing early disease symptoms before they spread. This research focuses on detecting diseases in chili plants using neural network architectures via transfer learning, specifically MobileNetV2 and VGG16, to classify chili leaf images. The study aims to identify three disease classes: begomovirus, leaf spots, and healthy leaves. The dataset comprises 3,150 leaf images, split into 70% for training and 30% for testing. Results show that MobileNetV2 achieved an accuracy of 99.47% and VGG16 98.62%, with evaluation using a confusion matrix indicating good performance in disease identification, where MobileNetV2 offers better computational efficiency. Thus, transfer learning can effectively identify leaf diseases in chili plants.