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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST)
Nani Astuti Triana
Informatika STMIK Handayani Makassar
Computer Science & IT

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Accuracy of Kidney Disease Expert System Based On Certainty Factor and Dempster Shafer Algorithm Agussalim; Nani Astuti Triana; Eristya Maya Safitri; Anita Wulansari; Seftin Fitri Ana Wati
IJCONSIST JOURNALS Vol 3 No 2 (2022): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v3i2.66

Abstract

The information technology developments today resulted in several innovations, including the existence of an Expert System. The systems help diagnose diseases without directly meeting with experts/doctors. Many researchers have proposed algorithms to improve the accuracy of the expert system to approach the diagnosis by experts/doctors, including certainty factor and dempster shader. This study compares the algorithm's accuracy with the results of expert diagnosis of kidney disease. The expert system was developed using UML and a web-based version. From the comparison results, the dempster shaver algorithm has an accuracy rate of 80%, while the certainty factor is 60% compared to expert diagnoses.
Co-Authors Agussalim, Agussalim Anita Wulansari Eristya Maya Safitri Seftin Fitri Ana Wati