Design of Facial Expressions Recognition for Academic Presence By Using Backpropagation Artificial Neural Networks Based on Principal Components Analysis

Setiawati Setiawati; Uswatun Az Zahro; Nuzla Af'idatur Robbaniyyah; Ivan Luthfi Ihwani

doi:10.29303/semeton.v1i2.240

Authors

Setiawati Setiawati Department of Mathematics, University of Mataram
Uswatun Az Zahro Department of Mathematics, University of Mataram
Nuzla Af'idatur Robbaniyyah Department of Mathematics, University of Mataram
Ivan Luthfi Ihwani Department of Mathematics, National Central University and Department of Mathematics, Universitas Gadjah Mada

DOI:

https://doi.org/10.29303/semeton.v1i2.240

Keywords:

artificial neural networks, backpropagation, principal component analysis

Abstract

There are 4,004 universities in Indonesia where each university needs data to find out student activities, one of which is through attendance. Some universities in Indonesia still use manual attendance systems and attendance systems through the website. This system has several obstacles that require solutions. Therefore, a more concise system is needed to assist students in filling in the attendance. This research aims to make a design to make academic presence for students by using neural network. There are many methods that can be used to create this system including using Principal Component Analysis (PCA) based on Backlpropagation Neural Network (BNN) because it can help the system perform faster and more accurately without losing important information. After carrying out a series of steps of algorithm designed for student attendance, we get the recognition of facial image expressions by using ANN Backpropagation and recognition of facial image expression with PCA.

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