ANALYSIS OF MACHINE LEARNING METHODS FOR GENDER AND AGE IDENTIFICATION

An automatic individual identification system is needed to support the forensic odontology process more efficiently and easily because there is still opportunity to be developed. The purpose of this research was to analyze the machine learning method for gender and age identification based on mandibular parameters in panoramic radiography. The machine learning methods used are MLP (Multilayer Perceptron), Decision Tree, Naive Bayes, k-NN (Nearest Neighbors), Logistic Linear, and SVM (Support Vector Machine). The data for this research were taken from the Dental and Oral Hospital, Faculty of Dentistry, Universitas Airlangga Surabaya. The data consisted of 120 patients based on the validation results of radiology experts, consisting of 61 males and 59 females, and was divided into 104 training data, and 16 testing data. The mandibular image on panoramic radiography was measured for nine parameters, namely ramus height left (x₁ ), ramus height right (x₂ ), ramus length left (x₃ ), ramus length right (x₄ ), bigonial width (x₅ ), bicondylar breadth (x₆ ), anterior mandibular corpus height left (x₇ ), anterior mandibular corpus height right (x₈ ), mandibular corpus length (x₉ ) using the ImageJ application by radiology experts. The best machine learning method for gender identification is k-NN, with evaluation values of accuracy, precision, recall, and f1 score, respectively, of 0.750, 0.764, 0.750, and 0.733. And the best method for age identification is MLP, with values of accuracy, precision, recall, and f1 score, respectively, of 0.625, 0.267, 0.350, and 0.297.