Klasifikasi Wajah Manusia Menggunakan Multi Layer Perceptron - Jefiza, Adlian; Diono, Diono; Putra, Irwanto Zarma; Budiana, Budiana; Nur Suciningtyas, Ika Karlina Laila; Siregar, Lindawani; Puspita, Widya Rika; Harlan, Fandy Bestario; Assegaf, Iqchan; Marpaung, Roy Hitmen (JURNAL INTEGRASI, 2023)
The problem of data security at a time when it is needed in the world of technology. The use of biometrics as data security is very necessary. This study aims to detect human biometrics using the Kinect sensor. The biometric that is detected is the face. The face image is captured by the Kinect sensor. For data feature extraction using Gray Level Co-Occurrence Matrix (GLCM. The parameters used are Contrast, Energy, Homogenity, and Correlation. The data obtained will be classified using Multi Layer Perceptron. Face classification is based on race. There are 3 races studied namely Indonesian, Chinese and African Native Races. The total data used are 100 photos of faces. The classification results show an accuracy of 86.7% using Multi Layer Perceptron