KLASIFIKASI SAPI TERNAK NORMAL DAN TERINFEKSI LUMPY SKIN DISEASE MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

RAMJA, AQSHAL DINATA and Miraswan, Kanda Januar (2025) KLASIFIKASI SAPI TERNAK NORMAL DAN TERINFEKSI LUMPY SKIN DISEASE MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Lumpy Skin Disease (LSD) is a contagious disease in cattle that causes significant losses in the livestock sector, making early detection crucial. This study aims to classify cattle into normal and LSD-infected categories using the Convolutional Neural Network (CNN) method with the ResNet50 architecture. The dataset consists of 4000 images, divided into 80% training data, 10% validation data, and 10% testing data. The model was trained to recognize visual features of infected cattle and demonstrated excellent performance, achieving an average accuracy of 96.75%, precision of 96.78%, recall of 96.75%, and F1-score of 96.76%. These results indicate that the ResNet50 CNN architecture is effective for detecting Lumpy Skin Disease in cattle and has the potential to be implemented as an automated diagnostic tool in the livestock industry.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Lumpy Skin Disease, Klasifikasi Citra, CNN, ResNet50, Deteksi Penyakit Ternak
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Aqshal Dinata Ramja
Date Deposited:	04 Jul 2025 03:11
Last Modified:	04 Jul 2025 03:11
URI:	http://repository.unsri.ac.id/id/eprint/176748

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