Skripsi

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

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Penilaian

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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

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307001894	T105211	T1052112023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1052112023

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xiv, 102 hlm.; ilus.; 29 cm

Language

ISBN/ISSN

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Classification

005.707

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Data dalam sistem-sistem komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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