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A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 5 No. 1 (2023) · hlm. 542–556

DOI
10.37385/jaets.v5i1.1839
ISSN
2715-6079, 2715-6087
Terbit
2023-12-10
Penerbit
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Bahasa
en

Abstrak

One part of forensic anthropology involves investigating skeletal remains to identify corpses, and many of these remains were found incomplete, burned, broken, or destroyed, making investigation challenging. This study aims to use the pelvis and femur to identify the gender of skeletal remains. The pelvis and femur have previously been proven to be accurate indicators of a corpse's gender. The identification process is done through the measurement of the subpubic angle of the pelvis and the angle taken straight down from the top of the femur to the patella and then straight up. The two measurements were combined using the principal component analysis (PCA) method into two attributes on the x and y axes. These attributes were later used as data for the machine learning model design. The design process consisted of an Artificial Neutral Network (ANN) design model and Support Vector Machine (SVM) design model combined into a hybrid machine learning system. The ANN and SVM hybrid machine learning were tested with acquired data. The result of the test using the confusion matrix showed 83.33% accuracy, which is categorized as "good classification" based on Area Under the Curve (AUC).

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  • Riau University
  • Institut Bisnis dan Teknologi Pelita Indonesia
  • University of Technology Malaysia
  • Universitas Sains dan Teknologi Indonesia
  • Universitas Halu Oleo
  • King Abdulaziz University
  • Telkom University
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Rekan penulisOrganisasiArtikel bersama
Razali NgahUniversity of Technology Malaysia2
Luthfi AfifRiau University2
Khusaeri AndesaUniversitas Sains dan Teknologi Indonesia1
Agustin AgustinUniversitas Sains dan Teknologi Indonesia1
Indah Permata SariRiau University1
Dara Incam RamadhanRiau University1
Jafar AhiriUniversitas Halu Oleo1
Samiruddin SamiruddinUniversitas Halu Oleo1
Muhammad Khairul AnamUniversitas Sains dan Teknologi Indonesia1
Muhammad FirdausMulawarman University1
Ahmad ZamsuriUniversitas Lancang Kuning1
Arda YuniantaKing Abdulaziz University1
Hasan J. AlyamaniKing Abdulaziz University1
Muhammad RidhaState University of Malang1
Mahmud Dwi SulistiyoTelkom University1
Muhammad RakhaTelkom University1
Kurnia AlfikriRiau University1
Rhendy Theopilus SilalahiRiau University1
Irfan Alwandi PohanRiau University1
Zikri PradanaRiau University1
Rahyul AmriRiau University1
Gilang BagaskaraRiau University1
Taufiq MuammarRiau University1
Rahyul AmriRiau University1
Muhammad Rizki RadhelanRiau University1
Indra YasriRiau University1
Feri CandraRiau University1
Ahmad Mudhirullaq SyamAfiliasi tidak diketahui1
Muhammad IkmalRiau University1
Daniel JuniantoRiau University1
Immanuel WaruwuRiau University1
Ping Jack SohUniversity of Oulu1
Yoshihide YamadaUniversity of Technology Malaysia1
Yoga Bayu PradanaRiau University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers </dc:title>
	<dc:creator xml:lang="en">Nasien, Dewi</dc:creator>
	<dc:creator xml:lang="en">Adiya, M. Hasmil</dc:creator>
	<dc:creator xml:lang="en">Rahayu, Yusnita</dc:creator>
	<dc:creator xml:lang="en">Dahliyusmanto, Dahliyusmanto</dc:creator>
	<dc:creator xml:lang="en">Erlin, Erlin</dc:creator>
	<dc:creator xml:lang="en">Anggara, Devi Willieam</dc:creator>
	<dc:subject xml:lang="en">ANN-SVM</dc:subject>
	<dc:subject xml:lang="en">PCA</dc:subject>
	<dc:subject xml:lang="en">Pelvic</dc:subject>
	<dc:subject xml:lang="en">Femur</dc:subject>
	<dc:subject xml:lang="en">Gender_Determination</dc:subject>
	<dc:description xml:lang="en">One part of forensic anthropology involves investigating skeletal remains to identify corpses, and many of these remains were found incomplete, burned, broken, or destroyed, making investigation challenging. This study aims to use the pelvis and femur to identify the gender of skeletal remains. The pelvis and femur have previously been proven to be accurate indicators of a corpse's gender. The identification process is done through the measurement of the subpubic angle of the pelvis and the angle taken straight down from the top of the femur to the patella and then straight up. The two measurements were combined using the principal component analysis (PCA) method into two attributes on the x and y axes. These attributes were later used as data for the machine learning model design. The design process consisted of an Artificial Neutral Network (ANN) design model and Support Vector Machine (SVM) design model combined into a hybrid machine learning system. The ANN and SVM hybrid machine learning were tested with acquired data. The result of the test using the confusion matrix showed 83.33% accuracy, which is categorized as "good classification" based on Area Under the Curve (AUC).</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2023-12-10</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://journal.yrpipku.com/index.php/jaets/article/view/1839</dc:identifier>
	<dc:identifier>10.37385/jaets.v5i1.1839</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS); 542-556</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v5i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/1839/2134</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Journal of Applied Engineering and Technological Science (JAETS)</dc:rights>
</oai_dc:dc>