Pengenalan Wajah Menggunakan PCA dengan Memperhatikan Jumlah Data Latih dan Vektor Eigen

Authors

  • Rifki Kosasih Gunadarma University

DOI:

https://doi.org/10.32493/informatika.v6i1.7261

Keywords:

Principle Component Analysis (PCA), feature, Manifold Learning, eigenface, eigenvector

Abstract

To find out if an employee is present, attendance is usually used. Attendance can be done in several ways, one of which is by filling in the attendance list that has been provided (manual attendance). However, this method is less effective because there is a possibility that employees who are not present will entrust attendance to employees who are present. Therefore, other ways are needed so that this does not happen. In this study, attendance was carried out using facial recognition. Face recognition is one of the fields used to recognize someone. A person's face usually has special characteristics that are easily recognized by people. These special characteristics are also called features. In this study, these features can be searched using the Principle Component Analysis (PCA) method. The PCA method is one of the methods used to produce features by reducing dimensions using eigenvectors from facial images (eigenface). The facial image used in this study consisted of 40 people with each person having 10 facial images with various expressions. Image data is divided into two parts, namely training data and test data. In this study, it is proposed to pay attention to the amount of training data and the number of eigenvectors used to get the best level of accuracy. From the research results, the highest level of accuracy occurs when the training data for each person is 7 and the test data for each person is 3 with an accuracy rate of 96.67%.

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Jurnal Informatika Universitas Pamulang Vol. 6 No. 1 Maret 2021

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Published

2021-03-31

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Vol. 6 No. 1 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

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