A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers

Authors

  • Dewi Nasien Institut Bisnis dan Teknologi Pelita Indonesia
  • M. Hasmil Adiya Institut Bisnis dan Teknologi Pelita Indonesia
  • Yusnita Rahayu Universitas Riau
  • Dahliyusmanto Dahliyusmanto Universitas Riau
  • Erlin Erlin Institut Bisnis dan Teknologi Pelita Indonesia
  • Devi Willieam Anggara Universiti Teknologi Malaysia

DOI:

https://doi.org/10.37385/jaets.v5i1.1839

Keywords:

ANN-SVM, PCA, Pelvic, Femur, Gender_Determination

Abstract

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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Published

2023-12-10

How to Cite

Nasien, D., Adiya, M. H., Rahayu, Y., Dahliyusmanto, D., Erlin, E., & Anggara, D. W. (2023). A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers . Journal of Applied Engineering and Technological Science (JAETS), 5(1), 542–556. https://doi.org/10.37385/jaets.v5i1.1839