Klasifikasi Penyakit Infeksi Pada Ayam Berdasarkan Gambar Feses Menggunakan Convolutional Neural Network
DOI:
https://doi.org/10.31598/sintechjournal.v5i2.1179Keywords:
Artifical Intelligence, Deep Learning, Keras/TensorFlow, Infectious Disease, Feces ImagesAbstract
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
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