PERANCANGAN USER INTERFACE BERBASIS WEBSITE DENGAN MENGIMPLEMENTASIKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK MENDETEKSI PENYAKIT TUBERKULOSIS MENGGUNAKAN CITRA CHEST X-RAY

AGAM, REGAN and Dwijayanti, Suci (2023) PERANCANGAN USER INTERFACE BERBASIS WEBSITE DENGAN MENGIMPLEMENTASIKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK MENDETEKSI PENYAKIT TUBERKULOSIS MENGGUNAKAN CITRA CHEST X-RAY. Undergraduate thesis, Sriwijaya University.

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Abstract

Convolutional Neural Network (CNN) is a part of deep learning commonly used for image classification. There are many studies that utilize CNN for classifying tuberculosis and covid-19, as well as normal conditions, using chest x-ray images. However, these studies are still rarely implemented on Indonesian data. Furthermore, the CNN models built have not been deployed in the form of a user interface that can be used by health workers. In this study, three CNN architectures, namely AlexNet, LeNet, and a modified architecture, are used to classify tuberculosis, covid-19, and normal conditions by training them on a dataset that combines Indonesia and Kaggle datasets. The results show that the AlexNet architecture is the best architecture with the highest accuracy of 97.52% on the Kaggle dataset, 64.45% for the RSUP dr. Rivai Abdullah dataset, and 92.43% for the combined dataset. This model is then used for deployment in a user interface. During testing using new data from RSUP dr. Rivai Abdullah, the model embedded in the website was able to detect 7 out of 10 new data with an accuracy percentage of 70%. The web-based user interface, built using the Gradio library, is capable of providing an initial diagnosis for patients to assist medical staff.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Indonesian Dataset, CNN Model, AlexNet, LeNet, Self-Modified Architecture, Deployment
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK1 Electrical engineering--Periodicals. Automatic control--Periodicals. Computer science--Periodicals. Information technology--Periodicals. Automatic control. Computer science. Electrical engineering. Information technology.
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Regan Agam
Date Deposited:	14 Jul 2023 02:13
Last Modified:	14 Jul 2023 02:13
URI:	http://repository.unsri.ac.id/id/eprint/116859

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