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All Journal Conference SENATIK STT Adisutjipto Yogyakarta JISA (Jurnal Informatika dan Sains) Informatics and Enginering Dedication Journal Of Artificial Intelligence And Software Engineering The Indonesian Journal of Computer Science Jurnal Informatika
Arif, Alfis
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Other

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Journal : Journal Of Artificial Intelligence And Software Engineering

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Comparison of K-Nearest Neighbor and Naive Bayes Algorithms for Tuberculosis Diagnosis Classification Setiadi, Dedi; Arif, Alfis; Oktaria, Anik
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6456

Abstract

Tuberculosis is an infectious disease caused by the bacteria mycobacterium tuberculosis. Tuberculosis is a serious global health problem and can cause death if not treated properly. At the Sidorejo Health Center, the current process of diagnosing patients uses several benchmarks of medical history obtained from patients regarding complaints, symptoms, and risk factors, while the results of the diagnosis calculation are not yet known. Comparison of the K-nearest neighbor and naïve bayes algorithms in classifying tuberculosis can provide input for the Sidorejo Health Center in seeing the accuracy of the diagnosis of tuberculosis, with medical information such as symptoms and medical history, where later patient data will be processed using the rapid miner application. The system development method used in this study is CRISP-DM, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The testing method uses a confusion matrix to measure the accuracy of the algorithm model with the results being that the K-nearest neighbor algorithm produces a high accuracy of 98% while the naïve bayes algorithm is the lowest with an accuracy of 0.70%.
Co-Authors Anggriani, Yesi Pitaloka Dedi Setiadi Efan Febriansyah . Ferry Putrawansyah, Ferry Oktaria, Anik Puspita, Desi Rahmadayanti, Fitria Rony, Zahara Tussoleha Seftia, Monicka