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All Journal Jurnal Informatika Terpadu
Alimi, Aldi Akbar
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Pengembangan Sistem Deteksi Tuberkulosis pada Citra X-Ray Menggunakan Metode Convolutional Neural Network (CNN) dengan Framework Laravel Alimi, Aldi Akbar; Adriansyah, Ahmad Rio; Prima, Pudy
Jurnal Informatika Terpadu Vol 10 No 2 (2024): September, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i2.1437

Abstract

Tuberculosis or TB is a disease caused by Mycobacterium Tuberculosis, which has a high transmission level. TB disease can be diagnosed through several methods, namely, using sputum samples and using x-ray scans. However, both methods take a long time to detect. Therefore, a detection system is needed to detect TB disease quickly and can be done by anyone. This research creates a detection system that can detect TB disease through chest x-ray images. The detection system is a web-based application built using the Laravel framework and a machine learning model with the Convolutional Neural Network (CNN) method for X-ray image analysis. This research will apply the CNN model that has been made into a web-based application through an API created using the FastAPI framework. The results of research on the detection system show that the detection system can detect TB disease. Proven by the results of testing conducted using the black box testing method, the test results show that the test success rate is 87%. In addition, the machine learning model with the CNN method can also provide classification on x-ray images well, where an accuracy of 93% is obtained on training data and 85% on test data.
Co-Authors adriansyah, ahmad rio Prima, Pudy