Klasifikasi Penyakit Infeksi Pada Ayam Berdasarkan Gambar Feses Menggunakan Convolutional Neural Network

Abstract

Convolutional Neural Network (CNN) is one of the Deep Learning methods that is able to carry out an independent learning process that is popular and appropriate in classifying. The development of technology in the field of Deep Learning, this study aims to assist farmers in identifying the types of infectious diseases that attack chickens based on faecal images using Convolutional Neural Network (CNN) so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bacteria, and coccidiosis caused by parasites. To identify, it is necessary to classify the types of diseases that attack by using images of chicken feces. With deep learning using Keras/TensorFlow, 95.40% of chicken feces images are predicted to be infected with coccidiosis, 94.97% chicken feces images are predicted to be healthy, 90.21% chicken feces images are predicted to be infected with tetelo disease, and 96.50% chicken feces images are predicted to be infected with pullorum disease

Implementation of Transfer Learning in CNN for Classification of Nut Type

“…Another research was conducted by Moch Kholil using CNN, namely to classify infection diseases in chickens based on feces images using CNN. In this study, 95.40% of chicken feces images were predicted to have coccidiosis, 94.97% of chicken feces images were predicted to be healthy, 90.21% of chicken feces images were predicted to have Newcastle disease and 96.50% of chicken feces images were suspected of having pullorum disease (Kholil et al, 2022). CNN research was also conducted by D. Iswantoro to classify corn plant diseases using the CNN method with the number of input images of 150x150 getting an accuracy of 97.5% for training data, while for testing data with 50 data resulted in an accuracy rate of 94% (Iswantoro & Handayani UN, 2022).…”