KLASIFIKASI TUBERKULOSIS (TBC) DARI CITRA X-RAY MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

AL FATIH, MUHAMMAD SYAFIQ and Fachrurrozi, Muhammad (2024) KLASIFIKASI TUBERKULOSIS (TBC) DARI CITRA X-RAY MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Tuberculosis (TB) is one of the world's deadliest infectious diseases, affecting millions of people each year. Rapid and accurate diagnosis of TB is essential for effective treatment and control of the spread of the disease. This study develops a TB classification model from lung X-ray images using the Convolutional Neural Network (CNN) method with MobileNetV2 architecture. The dataset used consists of secondary data taken from the internet and primary data obtained from local hospitals. The model was trained with secondary data and tested using validation data (20% of the secondary dataset) and primary data from local hospitals. The results showed that the MobileNetV2 model achieved the best accuracy of 97.80% with 92.83% precision, 97.18% recall, and 94.95% F1-score on the validation data. However, when tested with local primary data, the model accuracy decreased to below 50%, indicating that variations in data quality and characteristics between local and secondary datasets affect the model performance. Th

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Tuberkulosis, Convolutional Neural Network, MobileNetV2, Klasifikasi Citra X-ray, Data Augmentasi, Diagnosis Medis.
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Syafiq Al Fatih
Date Deposited:	04 Nov 2024 05:07
Last Modified:	04 Nov 2024 05:07
URI:	http://repository.unsri.ac.id/id/eprint/159045

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