@thesis{thesis,
		author={Nurmaini  Siti and UNIGHA  SITI LUTHFIA},
		title ={KLASIFIKASI ABNORMALITAS JANTUNG ANAK
DENGAN ARSITEKTUR CONVOLUTIONAL
NEURAL NETWORKS BINARI DAN MULTI-KELAS},
		year={2023},
		url={http://repository.unsri.ac.id/105211/},
		abstract={This study aims to develop a classification model using Convolutional Neural Networks (CNN) architecture to identify heart abnormalities in children. In this study, binary and multi-class CNNs are used to process data from children's heart images and produce abnormality class predictions. The data used in this study comes from two categories: normal hearts and hearts with abnormalities. The results of the study show that both CNN models (binary and multi-class) successfully classified children's heart images with a high level of accuracy. The best performance achieved in the case of classifying abnormalities in Infant is by ResNet101 with an accuracy of 94.75% for the abnormality class, while the accuracy for the preview class is 99%. For the unseen data in the view class, the obtained accuracy is 94.2%, and for the unseen data in the abnormality class, the obtained accuracy is 94.75%. In conclusion, the results of this study show that Convolutional Neural Networks architecture can be used to classify heart abnormalities in children with a high level of accuracy. This model can be a useful tool in quickly and accurately diagnosing heart abnormalities in children}
		}