PERBANDINGAN METODE DEMPSTER SHAFER DAN DECISION TREE UNTUK DIAGNOSA VIRUS INFLUENZA PADA MANUSIA

MAULINA, SALSABELA and Utami, Alvi Syahrini and Rodiah, Desty (2023) PERBANDINGAN METODE DEMPSTER SHAFER DAN DECISION TREE UNTUK DIAGNOSA VIRUS INFLUENZA PADA MANUSIA. Undergraduate thesis, Sriwijaya University.

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Abstract

Influenza is a disease caused by the influenza virus that can easily attack anyone. In this study compared the Dempster Shafer and Decision Tree methods for the diagnosis of influenza disease in humans. The Decision Tree method establishes rules for diagnosing influenza in humans as a support in making decisions about the disease suffered. Uncertainty calculations are needed in expert systems so that system diagnostic results can be as accurate as an expert. One method for performing uncertainty calculations is to use the Dempster Shafer method. So the purpose of this study is to find a comparison of the Dempster Shafer and Decision Tree methods for the diagnosis of influenza which is obtained through experts totaling 17 symptoms and 3 types of diseases. This study used 61 test data obtained from the practice of Dr. Nyayu Aisyah. From the test results, the Dempster Shafer method obtained an accuracy percentage of 65% while the Decision Tree method obtained an accuracy percentage of 91%. It can be concluded that the Decision Tree method is better than the Dempster Shafer method in diagnosing influenza.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Decision Tree, Dempster Shafer, Influenza
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Salsabela Maulina
Date Deposited:	26 Jun 2023 06:16
Last Modified:	26 Jun 2023 06:16
URI:	http://repository.unsri.ac.id/id/eprint/112883

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