Classification of Cattle Diseases in Semin District Using Convolutional Neural Network (CNN) - Permana, Xvan Erik Kobar; Rozaq Rais, Nendy Akbar; Muqorobin, Muqorobin (International Journal of Computer and Information System (IJCIS), 2024)
Cattle farming is a crucial sector for the economy and food security in Semin District. However, cattle diseases pose a serious threat, leading to economic losses and animal welfare issues. Farmers' lack of understanding about cattle diseases hinders effective disease management, and some solutions implemented by farmers can worsen the condition of the animals. Therefore, this study aims to implement a disease classification system for cattle using Convolutional Neural Network (CNN). The diseases targeted in this study include three common threats to cattle in this region: Bovine Ephemeral Fever (BEF), Mastitis, and Scabies. With the advancement of technology, it is expected that cattle farmers in Semin District can minimize errors in diagnosing cattle diseases through the application of artificial intelligence (AI) for disease classification. The study utilized a dataset consisting of 864 training data and 216 validation data, achieving an accuracy of 1.0000 and a loss of 0.0040. For testing, the system achieved an accuracy of 0.9306 and a loss of 0.4430.