Klasifikasi Citra CT-Scan Paru-Paru Menggunakan Hybrid Golden Eagle Optimizer dan Convolutional Neural Network

Baihaki, Rifki Ilham (2022) Klasifikasi Citra CT-Scan Paru-Paru Menggunakan Hybrid Golden Eagle Optimizer dan Convolutional Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember.

	Text Tesis - Rifki Ilham Baihaki - 06111950012004.pdf - Accepted Version Restricted to Repository staff only until 1 April 2024. Download (4MB) | Request a copy
	Text Tesis - Rifki Ilham Baihaki - 06111950012004.pdf Restricted to Repository staff only Download (4MB) | Request a copy

Abstract

Computed Tomography Scanner atau biasa dikenal dengan CT-Scan pertama kali diperkenalkan ke klinik medis pada tahun 1972 oleh EMI. Pada awalnya CT-Scan hanya digunakan untuk mencitrakan otak manusia. Tetapi semakin berkembangnya ilmu pengetahuan teknologi juga membuat CT-Scan digunakan untuk mencitrakan organ tubuh manusia lainnya. Salah satu organ tubuh manusia yang biasa dicitrakan menggunakan CT-Scan adalah paru-paru. Penyakit paru-paru yang saat ini sedang mengglobal adalah COVID-19. Penyakit COVID-19 memiliki kemiripan dengan Viral Pneumonia. Sehingga diperlukan teknik yang tepat untuk mengklasifikasi COVID-19 dengan Viral Pneumonia. Klasifikasi citra CT-Scan paru-paru pasien penderita COVID-19, pasien penderita Viral Pneumonia dan paru-paru normal dilakukan dengan Convolutional Neural Network (CNN). Hasil klasifikasi CNN bergantung pada arsitektur jaringan yang digunakan. Untuk menemukan arsitektur CNN terbaik, digunakan algoritma Golden Eagle Optimizer. Berdasarkan pengujian yang dilakukan akurasi pelatihan CNN terbaik dihasilkan dengan arsitektur CNN dengan 8 layer yaitu sebesar 100 persen. Nilai akurasi pengujian yangdihasilkan arsitektur tersebut adalah sebesar 95 persen.
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Computed Tomography Scanner or CT-Scan was first introduced to medical
clinics in 1972 by EMI. At the first, CT-Scan was only used to image the human
brain. But the development of science and technology also makes CT-Scan used to
image other human organs. One of the organs of the human body that is usually
imaged using a CT-Scan is the lungs. The lung disease that is currently going global
is COVID-19. The disease COVID-19 has similarities to Viral Pneumonia. So we
need the right technique to classify COVID-19 with Viral Pneumonia. The
classification of CT-Scan images of the lungs of patients with COVID-19, patients
with Viral Pneumonia and normal lungs was carried out using the Convolutional
Neural Network (CNN). CNN classification results depend on the network
architecture used. To find the best CNN architecture, the Golden Eagle Optimizer
algorithm is used. Based on the tests carried out, the best CNN training accuracy is
produced with a CNN architecture with 8 layers, which is 100 percent. As for the
accuracy of the CNN test, the testing accuracy is 95 percent.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	image of CT-Scan lungs, image classification, Convolutional Neural Network, Golden Eagle Optimizer, Citra CT-Scan paru-paru, klasifikasi citra, Convolutional Neural Network
Subjects:	R Medicine > R Medicine (General) R Medicine > R Medicine (General) > R858 Deep Learning T Technology > T Technology (General) > T57.5 Data Processing T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44101-(S2) Master Thesis
Depositing User:	Rifki Ilham Baihaki
Date Deposited:	18 Feb 2022 07:54
Last Modified:	18 Feb 2022 07:54
URI:	http://repository.its.ac.id/id/eprint/94388

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