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dc.contributor.authorSalsabila, Dewi Salma
dc.contributor.authorTanamal, Rinabi
dc.date.accessioned2020-11-26T06:33:05Z
dc.date.available2020-11-26T06:33:05Z
dc.date.issued2020-07-21
dc.identifier.issn2502-3470
dc.identifier.urihttp://dspace.uc.ac.id/handle/123456789/3079
dc.description.abstractShown symptoms in digestive diseases might be similar, resulting in patient’s suspected diseases before and after diagnosis attempt might turn out to be different. This paper aims to build a design of an expert system for digestive disease identification using Naïve Bayes methodology for iOS-based applications. The result from this paper helps medical interns to increase the accuracy in predicting patient’s suspected digestive disease. A precise prediction in suspected disease identification can minimalize unnecessary diagnosis attempts, which saves time and reduces cost. Naïve Bayes is chosen because it has a higher accuracy level than other classification methods. This research includes collecting data through literature reviews on digestive diseases and their symptoms, processing the data to be turned into a knowledge base for the expert system, conducting data training using Naïve Bayes by the designed expert system application through this research. The result from the conducted data training using Naïve Bayes methodology shows that the expert system application has a higher accuracy level, which is 84%.en_US
dc.language.isootheren_US
dc.publisherUnitomoen_US
dc.subjectdigestive disease; expert system; naïve Bayes; iOS; knowledge-baseden_US
dc.titleDesign of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Applicationen_US
dc.typeOtheren_US


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