Detection of Lumpy Disease in Livestock Using the MobileNetV2 Architecture Method - Putra, Dion Pratama; Wahyu Wiriasto, Giri; Paniran, Paniran (Jurnal Bumigora Information Technology (BITe), 2024)
Background: Lumpy Skin Disease (LSD) causes skin lesions, decreased milk production, and death in livestock such as cows. Objective: The purpose of this study is to detect LSD disease quickly and accurately using the Convolutional Neural Network (CNN) MobileNetV2 method based on android application. Method: This study uses a quantitative method with a reuse-oriented development approach and the MobileNetV2 algorithm trained with augmentation data and LSD disease image classification. Result: The results of this study are that the MobileNetV2 classification model is able to detect LSD with an accuracy of 95.91%. The developed application makes it easier for farmers to detect diseases early so that they can accelerate preventive measures. Conclusion: The implications of this study indicate that the MobileNetV2 model can improve the effectiveness of disease detection in livestock and can be applied in animal health applications in the field.