Dewantari, Diah Ayu Putri (2022) KLASIFIKASI PARU-PARU NORMAL DAN PNEUMONIA DENGAN CITRA X-RAY MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate (S1) thesis, Universitas Muhammadiyah Malang.

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Abstract

Pneumonia is a disease caused by bacteria and viruses that enter the body and infect the lungs. Exposure to contaminated air in this environment can cause inflammation, and fluid filling in the lungs, which can ultimately reduce the flow of oxygen to the bloodstream. Therefore X-Ray is a tool used for the diagnosis of pneumonia. The result of detecting pneumonia is using X-Ray images, where so far doctors are still having trouble analyzing the results of chest X-rays manually. This is considered less effective because there are still many errors or human errors that occur during lung detection. In this final project the author will develop a lung detection system using deep learning Convolutional Neural Networks (CNN). Where Convolutional Neural Networks can detect and recognize an object in the image. Therefore, in this study of normal lungs and lungs infected with pneumonia, we used the detection classification of the X-ray image of the lungs. At the stage of accuracy results using the CNN (Convolutional Neural Networks) method, it is hoped that it can detect the results of X-Ray images of normal lungs and lungs that are infected with pneumonia well. The testing process carried out on the classification system for normal lungs and pneumonia-infected lungs has an accuracy rate of 0.9703 and a training loss of 0.2805.

Item Type:	Thesis (Undergraduate (S1))
Student ID:	201710130311144
Thesis Advisors:	Mohammad Chasrun Hasani (0007086808), Novendra Setyawan (0719119201)
Keywords:	Pneumonia; X-Ray; machine learning; CNN; image processing
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering > Department of Electrical Engineering (20201)
Depositing User:	201710130311144 diahayu14
Date Deposited:	21 Jan 2022 06:17
Last Modified:	21 Jan 2022 06:17
URI :	http://eprints.umm.ac.id/id/eprint/83516

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