Klasifikasi Wajah Manusia Menggunakan Multi Layer Perceptron

  • Adlian Jefiza Politeknik Negeri Batam
  • Diono Diono Politeknik Negeri Batam
  • Irwanto Zarma Putra Politeknik Negeri Batam
  • Budiana Budiana Politeknik Negeri Batam
  • Ika Karlina Laila Nur Suciningtyas Politeknik Negeri Batam
  • Lindawani Siregar Politeknik Negeri Batam
  • Widya Rika Puspita Politeknik Negeri Batam
  • Fandy Bestario Harlan Politeknik Negeri Batam
  • Iqchan Assegaf Politeknik Negeri Batam
  • Roy Hitmen Marpaung Politeknik Negeri Batam


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


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