JOURNAL ARTICLE

Sistem Pakar Diagnosa Penyakit Pada Perokok menggunakan Metode Teorema Naive Bayes

Siti MuntariFebriansyah Febriansyah

Year: 2022 Journal:   Building of Informatics Technology and Science (BITS) Vol: 3 (4)

Abstract

The purpose of this research is to produce an expert system for diagnosing disease in smokers using the Naïve Bayes Theorem method. The problem that arises in this study is the process of determining the disease in smokers through the diagnosis of experts, patients must come to the hospital and see a doctor during working hours. In the development of this expert system using the waterfall SDLC method with the stages of Analysis, Design, Coding, Testing, and testing methods carried out in this Blackbox research. To determine the type of smoker's disease, this system uses the PHP MySQL Database programming language, and Dreamweaver uses. The results of this study are in the form of a website-based expert system that is able to help users or the public in diagnosing passive smoking and providing formations about smoking diseases. The results of the Blackbox Testing test have an average score of 4.2 with a valid category

Keywords:
Waterfall model Computer science Expert system Naive Bayes classifier Test (biology) Bayes' theorem Medicine Systems development life cycle Artificial intelligence Natural language processing Machine learning Software Programming language Software development process

Metrics

3
Cited By
0.59
FWCI (Field Weighted Citation Impact)
10
Refs
0.66
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Edcuational Technology Systems
Physical Sciences →  Computer Science →  Artificial Intelligence
Multimedia Learning Systems
Physical Sciences →  Computer Science →  Information Systems
Data Mining and Machine Learning Applications
Physical Sciences →  Computer Science →  Information Systems

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