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Automation of gender determination in human canines using artificial intelligence

Dental Journal (Majalah Kedokteran Gigi) · Vol. 50 No. 3 (2017) · hlm. 116–120

DOI
10.20473/j.djmkg.v50.i3.p116-120
ISSN
2442-9740, 1978-3728
Terbit
2017-09-30
Penerbit
Faculty of Dental Medicine, Universitas Airlangga https://fkg.unair.ac.id/en
Bahasa
eng

Abstrak

Background: Gender determination is an important aspect of the identification process. The tooth represents a part of the human body that indicates the nature of sexual dimorphism. Artificial intelligence enables computers to perform to the same standard the same tasks as those carried out by humans. Several methods of classification exist within an artificial intelligence approach to identifying sexual dimorphism in canines. Purpose: This study aimed to quantify the respective accuracy of the Naive Bayes, decision tree, and multi-layer perceptron (MLP) methods in identifying sexual dimorphism in canines. Methods: A sample of results derived from 100 measurements of the diameter of mesiodistal, buccolingual, and diagonal upper and lower canine jaw models of both genders were entered into an application computer program that implements the algorithm (MLP). The analytical process was conducted by the program to obtain a classification model with testing being subsequently carried out in order to obtain 50 new measurement results, 25 each for males and females. A comparative analysis was conducted on the program-generated information. Results: The accuracy rate of the Naive Bayes method was 82%, while that of the decision tree and MLP amounted to 84%. The MLP method had an absolute error value lower than that of its decision tree counterpart. Conclusion: The use of artificial intelligence methods produced a highly accurate identification process relating to the gender determination of canine teeth. The most appropriate method was the MLP with an accuracy rate of 84%.

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11 penulis · 16 tautan kepenulisan bersama · 2 lintas organisasi

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  • University of Brawijaya
  • Institut Teknologi Sepuluh Nopember
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Rekan penulisOrganisasiArtikel bersama
Fajar PradanaUniversity of Brawijaya6
Fitra Abdurrachman BachtiarUniversity of Brawijaya2
Siti RochimahInstitut Teknologi Sepuluh Nopember1
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember1
Barlian Henryranu P.Afiliasi tidak diketahui1
Wijaya KurniawanUniversity of Brawijaya1
Mochammad AdhyUniversity of Brawijaya1
Elke Cahya PutriUniversity of Brawijaya1
Denny SagitaUniversity of Brawijaya1

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Sumber metadata: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/MKG/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Automation of gender determination in human canines using artificial intelligence</dc:title>
	<dc:creator>Fidya, F.</dc:creator>
	<dc:creator>Priyambadha, Bayu</dc:creator>
	<dc:subject xml:lang="en-US">sexual dimorphism</dc:subject>
	<dc:subject xml:lang="en-US">canines</dc:subject>
	<dc:subject xml:lang="en-US">artificial intelligence</dc:subject>
	<dc:subject xml:lang="en-US">automation</dc:subject>
	<dc:description xml:lang="en-US">Background: Gender determination is an important aspect of the identification process. The tooth represents a part of the human body that indicates the nature of sexual dimorphism. Artificial intelligence enables computers to perform to the same standard the same tasks as those carried out by humans. Several methods of classification exist within an artificial intelligence approach to identifying sexual dimorphism in canines. Purpose: This study aimed to quantify the respective accuracy of the Naive Bayes, decision tree, and multi-layer perceptron (MLP) methods in identifying sexual dimorphism in canines. Methods: A sample of results derived from 100 measurements of the diameter of mesiodistal, buccolingual, and diagonal upper and lower canine jaw models of both genders were entered into an application computer program that implements the algorithm (MLP). The analytical process was conducted by the program to obtain a classification model with testing being subsequently carried out in order to obtain 50 new measurement results, 25 each for males and females. A comparative analysis was conducted on the program-generated information. Results: The accuracy rate of the Naive Bayes method was 82%, while that of the decision tree and MLP amounted to 84%. The MLP method had an absolute error value lower than that of its decision tree counterpart. Conclusion: The use of artificial intelligence methods produced a highly accurate identification process relating to the gender determination of canine teeth. The most appropriate method was the MLP with an accuracy rate of 84%.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Dental Medicine, Universitas Airlangga https://fkg.unair.ac.id/en</dc:publisher>
	<dc:date>2017-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/MKG/article/view/6155</dc:identifier>
	<dc:identifier>10.20473/j.djmkg.v50.i3.p116-120</dc:identifier>
	<dc:source xml:lang="en-US">Dental Journal (Majalah Kedokteran Gigi); Vol. 50 No. 3 (2017): September 2017; 116-120</dc:source>
	<dc:source>2442-9740</dc:source>
	<dc:source>1978-3728</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/MKG/article/view/6155/4559</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Dental Journal (Majalah Kedokteran Gigi)</dc:rights>
</oai_dc:dc>