Klasifikasi Jenis Kelamin Manusia Menggunakan Foto Panoramik Gigi Dengan Algoritma Learning Vector Quantization (LVQ)

• Main Author: Fariz Faqihuddin, (Author)
• Format: Book
• Published: 2020-07-06.
• Subjects: Thesis; NonPeerReviewed
• Online Access: Link Metadata
• Notes: http://repository.upnvj.ac.id/7099/1/ABSTRAK.pdf; http://repository.upnvj.ac.id/7099/2/AWAL.pdf; http://repository.upnvj.ac.id/7099/12/BAB%201.pdf; http://repository.upnvj.ac.id/7099/13/BAB%202.pdf; http://repository.upnvj.ac.id/7099/14/BAB%203.pdf; http://repository.upnvj.ac.id/7099/15/BAB%204.pdf; http://repository.upnvj.ac.id/7099/16/BAB%205.pdf; http://repository.upnvj.ac.id/7099/17/DAFTAR%20PUSTAKA.pdf; http://repository.upnvj.ac.id/7099/9/RIWAYAT%20HIDUP.pdf; http://repository.upnvj.ac.id/7099/10/LAMPIRAN.pdf; http://repository.upnvj.ac.id/7099/18/ARTIKEL%20KI.pdf
• Keywords: RK Dentistry; T Technology (General)

In forensic medicine in the identification especially in the body is unknown, damaged, rot, burned, mass accident, etc. that caused many deaths, or the case of babies exchanged, kidnapping the child using DNA as a reference because of its high accuracy value. However, the time it takes is quite long and often the victim is hard to get his body to take his DNA because the victim's body is damaged, burnt burned, etc. So the research aims to build a system for identification of human gender through a panoramic image of the tooth. Teeth are the toughest part of the human body, so the forensic team can be used to identify victims. Therefore, this research was done to create a system that serves as a classification of teeth to help distinguish the human gender that forensic teams can use to identify victims using the Grey Level Co - Occuration Matrix (GLCM) method for the analysis of tooth image textures And the Learning Vector quantization (LVQ) algorithm for classification of dental imagery with a training image of 15 human-tooth panoramic images of top right, bottom left, and lower right, then the total tooth image is researched as a training data as much as 120 kaninus tooth image. This test is done in order to know the level of accuracy of the methods and algorithms used so that it can be used to identify human teeth in distinguishing human gender. The study gained an accuracy rate of 78.125% on the Epoch 100 and the learning rate of 0.1 and 0.2.