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Saputri, Yunita Maulida
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Computer Science & IT

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Case Based Reasoning for Diagnosing Tuberculosis (TB) Saputri, Yunita Maulida; Nurdiansyah, Yanuar; Pandunata, Priza
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1759

Abstract

Tuberculosis, often referred to as TB, is a contagious disease caused by the bacterium Mycobacterium tuberculosis. TB primarily affects the lungs but can also affect other organs, a condition known as Extra-pulmonary TB. The disease is transmitted through the air, with the source of transmission being individuals with TB who are Acid-Fast Bacilli (AFB) positive and who sneeze or cough, releasing the bacteria into the air in the form of sputum droplets. TB can affect anyone. This research utilizes the Case- Based Reasoning (CBR) method to aid in the diagnosis of Tuberculosis. The diagnostic process involves inputting or selecting a new case that contains the symptoms to be diagnosed within the system. Then, the system calculates the similarity values between the new case and the cases stored in the case base using the Nearest Neighbor algorithm, normalized with the level of expert confidence. Testing was conducted using 50 cases from the case base and 38 new cases. The results of the system testing, using patient medical records and data obtained from literature studies, with diagnoses validated by experts, demonstrate that the system is capable of identifying 12 types of Tuberculosis with an accuracy rate of 92.3%.
Co-Authors Priza Pandunata Yanuar Nurdiansyah, Yanuar