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All Journal IJISTECH (International Journal Of Information System & Technology) JIIP (Jurnal Ilmiah Ilmu Pendidikan) IJISTECH
Firmansyah, Moch
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences Other

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Journal : IJISTECH (International Journal Of Information System

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Improve Accuracy in The Process of Diagnosing Various Types of Lung Diseases by Using The Naïve Bayes Classifier Firmansyah, Moch
IJISTECH (International Journal of Information System and Technology) Vol 7, No 2 (2023): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i2.305

Abstract

In the humans’ body, there are several organs that function to support humans’ life, one of these organs is lungs, in its development, many things happen to these organs, for examples, infected by various diseases, including the lungs. There are many types of diseases that can infect the lungs including Asthma, Dyspnea, Tuberculosis, COPD, Pneumonia, Bronchitis, Hemoptysis, Hemoptoe, and these diseases can be diagnosed based on the symptoms, but unfortunately there are difficulties in the classification process, because it has similar symptoms experienced by people with the disease. The purpose of this study is to be able to classify lung disease based on symptoms experienced using the navies bayes classification method. This method doesn’t use much training data in determining the estimated parameters used in the classification process. This is what makes researchers use this method. This study used patient medical records as many as 200 patient data. Data collection is carried out from February to May. Data testing using rapid miner tools resulted in 90.22% accuracy for lung disease diagnosis.
Co-Authors Moch Fuad Nasvian