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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Hafiyyan, Azka
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Implementation of The Naïve Bayes Algorithm For Diagnosing Lung Disesase In An Expert System Concept Hafiyyan, Azka; Seniwati, Erni; Haryoko, H
IJISTECH (International Journal of Information System and Technology) Vol 7, No 1 (2023): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i1.300

Abstract

Web-based lung disease diagnosis expert system is an application designedto help diagnose lung disease quickly and accurately. This system uses the Naïve Bayes classification method which is one of the techniques in artificial intelligence. The Naïve Bayes method utilizes probability to classify data based on existing attributes. In the context of a lung disease expert system, these attributes can include symptoms experienced by patients such as cough, shortness of breath, chest pain, and so on. Research results have shown that in the test of accuracy comparing the expert with the lung disease diagnostic system using the naive Bayes method, the accuracy rate reached 86.66%. With the achieved accuracy rate, it can be stated that the system is capable of performing diagnoses effectively and accurately
Implementation of The Naïve Bayes Algorithm For Diagnosing Lung Disesase In An Expert System Concept Hafiyyan, Azka; Seniwati, Erni; Haryoko, H
IJISTECH (International Journal of Information System and Technology) Vol 7, No 1 (2023): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i1.300

Abstract

Web-based lung disease diagnosis expert system is an application designedto help diagnose lung disease quickly and accurately. This system uses the Naïve Bayes classification method which is one of the techniques in artificial intelligence. The Naïve Bayes method utilizes probability to classify data based on existing attributes. In the context of a lung disease expert system, these attributes can include symptoms experienced by patients such as cough, shortness of breath, chest pain, and so on. Research results have shown that in the test of accuracy comparing the expert with the lung disease diagnostic system using the naive Bayes method, the accuracy rate reached 86.66%. With the achieved accuracy rate, it can be stated that the system is capable of performing diagnoses effectively and accurately
Co-Authors Haryoko, H Seniwati, Erni