KLASIFIKASI ABNORMALITAS JANTUNG ANAK DENGAN ARSITEKTUR CONVOLUTIONAL NEURAL NETWORKS BINARI DAN MULTI-KELAS

UNIGHA, SITI LUTHFIA and Nurmaini, Siti (2023) KLASIFIKASI ABNORMALITAS JANTUNG ANAK DENGAN ARSITEKTUR CONVOLUTIONAL NEURAL NETWORKS BINARI DAN MULTI-KELAS. Undergraduate thesis, Sriwijaya University.

	Text RAMA_56021_09011181924016.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_56201_09011181924016_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (14MB) | Request a copy
	Text RAMA_56201_09011281924016_0002085908_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (872kB)
	Text RAMA_56201_09011281924016_0002085908_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (268kB) | Request a copy
	Text RAMA_56201_09011281924016_0002085908_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (48kB) | Request a copy
	Text RAMA_56201_09011281924016_0002085908_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011281924016_0002085908_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6kB) | Request a copy
	Text RAMA_56201_09011281924016_0002085908_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (124kB) | Request a copy
	Text RAMA_56201_09011281924016_0002085908_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (294kB) | Request a copy

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

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	klasifikasi, Abnormal, Infant, Convolutional Neural Networks
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.6.A2-Z General works Simulation Cf. QA76.9.C65 Computer science Cf. TA343 Engineering mathematics
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Siti Luthfia Unigha
Date Deposited:	26 May 2023 02:28
Last Modified:	26 May 2023 02:28
URI:	http://repository.unsri.ac.id/id/eprint/105211

Actions (login required)

View Item