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Contact Name
Marsono Marsel.
Contact Email
idss@iocspublisher.org
Phone
+6281381251442
Journal Mail Official
idss@iocspublisher.org
Editorial Address
Romeby Lestari Housing Complex Blok C Number C14, North Sumatra, Indonesia
Location
Unknown,
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INDONESIA
Journal of Intelligent Decision Support System (IDSS)
ISSN : 27215792     EISSN : 27215792     DOI : -
Core Subject : Science,
An intelligent decision support system (IDSS) is a decision support system that makes extensive use of artificial intelligence (AI) techniques. Use of AI techniques in management information systems has a long history – indeed terms such as "Knowledge-based systems" (KBS) and "intelligent systems" have been used since the early 1980s to describe components of management systems, but the term "Intelligent decision support system" is thought to originate with Clyde Holsapple and Andrew Whinston in the late 1970s. Examples of specialized intelligent decision support systems include Flexible manufacturing systems (FMS),intelligent marketing decision support systems and medical diagnosis systems. Ideally, an intelligent decision support system should behave like a human consultant: supporting decision makers by gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating such proposed actions. The aim of the AI techniques embedded in an intelligent decision support system is to enable these tasks to be performed by a computer, while emulating human capabilities as closely as possible.
Articles 11 Documents
Search results for , issue "Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)" : 11 Documents clear
Application of fuzzy expert system method for early detection of dengue fever Prayoga, Alan Eka; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.217

Abstract

The application of the Fuzzy Expert System method in the early detection of dengue fever offers a promising approach to improve diagnostic accuracy. This study aims to develop a system that can overcome the diversity of dengue fever symptoms and uncertainty in the diagnosis process. Using medical record data of patients who have confirmed DHF, the study designed fuzzy rules for symptom evaluation, resulting in more precise diagnostic outputs. The results indicate the system's success in identifying dengue cases with high sensitivity and good positive predictive value. These findings confirm the importance of FES technology in clinical practice, especially for controlling and preventing dengue fever in endemic areas. Continued research will test this system in a broader clinical scenario to ensure its effectiveness and practicality in diverse medical environments.

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