IMPROVING RESNET-50 PERFORMANCE FOR CHICKEN DISEASE CLASSIFICATION BASED ON DUNG IMAGES - Andalantama, Yudikha; Hidayat, Tonny; Purwanto, Agus (JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 2025)
This study examines the application of the ResNet-50 model for categorizing chicken illnesses. The dataset utilized comprises 8,876 samples, which are classified into four main categories: healthy feces, Salmonella, Coccidiosis, and Newcastle disease. The dataset consists of 2,057 samples classified as healthy feces, 2,276 samples classified as Salmonella, 2,103 samples classified as Coc-cidiosis, and 2,440 samples classified as Newcastle disease. The implementation of the ResNet-50 model for analysis showcases outstanding performance, with a classification accuracy of 99.25%. This result affirms the model's exceptional ability to precisely identify poultry illnesses. The results of this study highlight the effectiveness of ResNet-50 in performing complex classification tasks and also provide a basis for future improvements. Considering the exceptional results, there are other aspects that can be improved upon to attain optimal performance. By integrating modern hyperparameter tuning approaches and incorporating diverse supplementary data, the model's generalization is expected to be improved, leading to higher accuracy in many real-world settings. Moreover, this will expand the practical applications of the approach in the veterinary and poultry sectors. This study greatly contributes to the diagnosis of diseases in poultry, relying on the findings obtained. It enables the potential for further progress that can improve the effectiveness of disease detection and prevention.