Performance analysis of support vector machine in sex classification of the sacrum bone in forensic anthropology.

• Main Authors: Haron, Habibollah, Iis Afrianty, Iis Afrianty, Dewi Nasien, Dewi Nasien
• Format: Article
• Language: English
• Published: Informatics Department, Universitas Jenderal Soedirman 2022
• Subjects: T Technology (General); T58.5-58.64 Information technology
• Online Access: http://eprints.utm.my/104483/1/LisAfriantyDewiNasienHabibollahHaron2022_PerformanceAnalysisofSupportVectorMachine.pdf; http://eprints.utm.my/104483/; http://dx.doi.org/10.15408/jti.v15i1.25254

Sex classification is part of forensic anthropological identification aimed at determining whether the skeleton belongs to a male or a female. This paper exhibits the performance of the Support Vector Machine (SVM) in classifying the sex of the sacrum in forensic anthropology. Bone data was measured by the metric method based on six variables, namely superior breadth, anterior length, mid ventral breadth, real height, diameter the base, and max-transverse diameter of the base. This study shows performance analysis of SVM using the library libSVM with linear, polynomial, and RBF kernel to observe the results of the comparison of the accuracy of the kernel used. According to the results of the trials, the best accuracy was attained in each kernel function, i.e., the RBF kernel is 83.33% with g = 1 and C = 1, the polynomial is 85.56% at γ = 2, C = 2 and d =1, and the linear kernel obtained best accuracy is 84.44 % with C = 2 and C = 3. In conformity with the experimental result, polynomial attained the highest accuracy of 85.56% at γ = 2, C = 2, and d =1.