Skripsi

KLASIFIKASI TINGKAT KEPARAHAN PRA KANKER SERVIKS PADA CITRA VIA MENGGUNAKAN CNN

---

Penilaian

---

The aim of this study is to classify cervicalpre cancer images using Convolutional Neural Network (CNN) method. The dataset of cervical precancer images was evaluated using various CNN models that implemented data augmentation of the second type. The research findings indicate that the CervicoNet model with 200 epochs, a learning rate of 10-3, and a batch size of 32 achieved the best accuracy of 93.75%. The model that provided the best unseen accuracy was the ResNet50 model with 100 epochs, a learning rate of 10-3, and a batch size of 32, achieving 68.88% accuracy on unseen data. The top three class accuracy results were obtained using the VGG 19 model with 100 epochs, a learning rate of 0.001, and a batch size of 32, achieving an accuracy of 99.6%. The results of this study are expected to contribute to the handling of cervical precancer through the application of deep learning techniques for the localization of cervical precancer images with improved accuracy.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2307005249	T126222	T1262222023	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1262222023

Publisher

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

Collation

xiv, 66 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

006.3 07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem Pakar
Jurusan Sistem Komputer

Specific Detail Info

-

Statement of Responsibility

ANUG

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment