Skripsi

PERBANDINGAN KINERJA KLASIFIKASI ABNORMALITAS JANTUNG JANIN DENGAN MENGGUNAKAN 10 STRUKTUR CONVOLUTIONAL NEURAL NETWORK

---

Penilaian

---

The use of Artificial Intelligence Technology in the field of Computer Vision has enabled the classification of fetal heart abnormalities by utilizing one of the Classification approaches. With technological advances, the image classification process can be implemented using Deep Learning (DL) models. This research uses the architecture of 10 Convolutional Neural Network Architectures for the Fetal Heart Abnormality Classification process. There are 10 Convolutional Neural Network Architectures namely Densenet121, Densenet169, Densenet201, InceptionV3, Resnet50, Resnet101, Resnet152, VGG16, VGG19, and Xception. The best Unseen Test performance is achieved by the Resnet101 model. Unseen performance results on Accuracy, Sensitivity, and Specificity evaluation metrics averaged 87.7%, 57% and 93.8%.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2307004183	T125586	T1255862023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1255862023

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xii, 322 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.650 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Jurusan Sistem Komputer
Jaringan Komunikasi Komputer

Specific Detail Info

-

Statement of Responsibility

SEPTA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment