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Comparing Fuzzy Logic Mamdani and Naí¯ve Bayes for Dental Disease Detection

Journal of Information Systems Engineering and Business Intelligence · Vol. 8 No. 2 (2022) · pp. 182–195

DOI
10.20473/jisebi.8.2.182-195
ISSN
2443-2555, 2598-6333
Published
2022-10-29
Publisher
Universitas Airlangga
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

Background: Dental disease detection is essential for the diagnosis of dental diseases. Objective: This research compares the Mamdani fuzzy logic and Naí¯ve Bayes in detecting dental diseases. Methods: The first is to process data on dental disease symptoms and dental support tissues based on complaints of toothache consulted with experts at a community health centre (puskesmas). The second is to apply the Mamdani fuzzy logic and the Naí¯ve Bayes to the proposed expert system. The third is to provide recommended decisions about dental diseases based on the symptom data inputted into the expert system. Patient data were collected at the North Cilacap puskesmas between July and December 2021. Results: The Mamdani fuzzy logic converts uncertain values into definite values, and the Naí¯ve Bayes method classifies the type of dental disease by calculating the weight of patients' answers. The methods were tested on 67 patients with dental disease complaints. The accuracy rate of the Mamdani fuzzy logic was 85.1%, and the Naí¯ve Bayes method was 82.1%. Conclusion: The prediction accuracy was compared to the expert diagnoses to determine whether the Mamdani fuzzy logic method is better than the Naí¯ve Bayes method. Keywords: Dental Disease, Expert System, Mamdani Fuzzy Logic, Naí¯ve Bayes, Prediction

Collaboration network of the authors

13 authors · 41 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Politeknik Negeri Cilacap
  • Politeknik Harapan Bersama
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dyah AprilianiPoliteknik Harapan Bersama1
Linda Perdana WantiAffiliation unknown1
Nur Wachid Adi PrasetyaAffiliation unknown1
Zahrun NafisaAffiliation unknown1
Muhammad RamadaniAffiliation unknown1
Rahmat HidayatPoliteknik Negeri Cilacap1
Nur Wachid Adi PrasetyaPoliteknik Negeri Cilacap1
Zahrun NafisaPoliteknik Negeri Cilacap1
Muhammad RamadaniPoliteknik Negeri Cilacap1
Santi PurwaningrumPoliteknik Negeri Cilacap1
Ratih Hafsarah MaharraniPoliteknik Negeri Cilacap1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Comparing Fuzzy Logic Mamdani and Naí¯ve Bayes for Dental Disease Detection</dc:title>
	<dc:creator>Wanti, Linda Perdana</dc:creator>
	<dc:creator>Somantri, Oman </dc:creator>
	<dc:description xml:lang="en-US">Background: Dental disease detection is essential for the diagnosis of dental diseases.
Objective: This research compares the Mamdani fuzzy logic and Naí¯ve Bayes in detecting dental diseases.
Methods: The first is to process data on dental disease symptoms and dental support tissues based on complaints of toothache consulted with experts at a community health centre (puskesmas). The second is to apply the Mamdani fuzzy logic and the Naí¯ve Bayes to the proposed expert system. The third is to provide recommended decisions about dental diseases based on the symptom data inputted into the expert system. Patient data were collected at the North Cilacap puskesmas between July and December 2021.
Results: The Mamdani fuzzy logic converts uncertain values into definite values, and the Naí¯ve  Bayes method classifies the type of dental disease by calculating the weight of patients' answers. The methods were tested on 67 patients with dental disease complaints. The accuracy rate of the Mamdani fuzzy logic was 85.1%, and the Naí¯ve Bayes method was 82.1%.
Conclusion: The prediction accuracy was compared to the expert diagnoses to determine whether the Mamdani fuzzy logic method is better than the Naí¯ve Bayes method.
 
Keywords: Dental Disease, Expert System, Mamdani Fuzzy Logic, Naí¯ve Bayes, Prediction</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2022-10-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/36690</dc:identifier>
	<dc:identifier>10.20473/jisebi.8.2.182-195</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 8 No. 2 (2022): October; 182-195</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/36690/22816</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 The Authors. Published by Universitas Airlangga.</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>