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Diagnose Disease Expert System Respiratory Tract Infection Method Using Certainty Factor

Authors

  • Aritana Lahagu STMIK Pelita Nusantara
  • Erwin Panggabean Lecturer Teknik Informatika, STMIK Pelita Nusantara, Medan

DOI:

10.47709/cnapc.v2i2.430

Keywords:

Respiratory tract infections, expert systems, certainty factor methods

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Abstract

Respiratory tract infections are infectious diseases that interfere with the process of human breathing. When the breathing process takes place, there are often various kinds of diseases, most of which can only be treated by a lung specialist. The arrival of a pulmonary specialist for consultation can take hours and is expensive. Then we need an expert system that can quickly find out the type of disease in human breathing and how to handle it and the solutions that will be provided. Expert system is a system that uses human knowledge to find out the system that is entered into a computer and then is used to solve problems that usually require expertise or human expertise. One application of an expert system to diagnose respiratory tract infections is to use the certainty factor method. The certainty factor method is a method used to solve problems from uncertain answers, and also produce uncertain answers. This uncertainty is influenced by two factors, namely uncertain rules and uncertain user answers. The research aims to build an expert system application for handling respiratory tract infection problems with Visual Studio 2010 as a tool for designing applications and using Microsoft Access 2007 Database as a database. This expert system is able to calculate similarity in weight calculation based on symptoms of respiratory tract infection using certainty factor methods and provide reports using crystal reports

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ARTICLE Published HISTORY

Submitted Date: 2019-11-28
Accepted Date: 2019-12-02
Published Date: 2020-06-01

How to Cite

Lahagu, A., & Panggabean, E. (2020). Diagnose Disease Expert System Respiratory Tract Infection Method Using Certainty Factor. Journal of Computer Networks, Architecture and High Performance Computing, 2(2), 263-267. https://doi.org/10.47709/cnapc.v2i2.430