Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System

Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System

Attendance is the fact that someone is present at an event or goes regularly to an institution, or attendance at an event is the number of people present at that time. The Saifiatul Amaliyah school itself is one of the many schools in Indonesia where the attendance of students or attendance is still...

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Bibliographic Details
Main Authors: Juliansyah Putra Tanjung, Bayu Angga Wijaya
Format: Article
Language:English
Published: Politeknik Ganesha Medan 2020-10-01
Series:Sinkron
Subjects:
face; k-nearest neighbor (k – nn); principal component analysis (pca); attendance; school;
Online Access:https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10612
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spelling doaj-1d351007cf6a461083e21751d48efd192020-11-25T03:59:57ZengPoliteknik Ganesha MedanSinkron2541-044X2541-20192020-10-0151435010.33395/sinkron.v5i1.10612589Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance SystemJuliansyah Putra Tanjung0Bayu Angga Wijaya1Universitas Prima Indonesia, IndonesiaUniversitas Prima Indonesia, IndonesiaAttendance is the fact that someone is present at an event or goes regularly to an institution, or attendance at an event is the number of people present at that time. The Saifiatul Amaliyah school itself is one of the many schools in Indonesia where the attendance of students or attendance is still done manually. This can cause problems, namely allowing fraud when filling in attendance and errors in data recapitulation. Therefore, in this study a computerized face attendance was created, which was formed using the K-Nearest Neighbor (K-NN) method and combined with the extraction of the Principal Component Analysis (PCA) feature where the attendance process can be done with a person's face. The face attendance system using the K-NN and PCA methods has an accuracy of 82%.https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10612face; k-nearest neighbor (k – nn); principal component analysis (pca); attendance; school;
collection DOAJ
language English
format Article
sources DOAJ
author Juliansyah Putra Tanjung
Bayu Angga Wijaya
spellingShingle Juliansyah Putra Tanjung
Bayu Angga Wijaya
Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System
Sinkron
face; k-nearest neighbor (k – nn); principal component analysis (pca); attendance; school;
author_facet Juliansyah Putra Tanjung
Bayu Angga Wijaya
author_sort Juliansyah Putra Tanjung
title Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System
title_short Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System
title_full Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System
title_fullStr Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System
title_full_unstemmed Facial Recognition Implementation using K–NN and PCA Feature Extraction in Attendance System
title_sort facial recognition implementation using k–nn and pca feature extraction in attendance system
publisher Politeknik Ganesha Medan
series Sinkron
issn 2541-044X
2541-2019
publishDate 2020-10-01
description Attendance is the fact that someone is present at an event or goes regularly to an institution, or attendance at an event is the number of people present at that time. The Saifiatul Amaliyah school itself is one of the many schools in Indonesia where the attendance of students or attendance is still done manually. This can cause problems, namely allowing fraud when filling in attendance and errors in data recapitulation. Therefore, in this study a computerized face attendance was created, which was formed using the K-Nearest Neighbor (K-NN) method and combined with the extraction of the Principal Component Analysis (PCA) feature where the attendance process can be done with a person's face. The face attendance system using the K-NN and PCA methods has an accuracy of 82%.
topic face; k-nearest neighbor (k – nn); principal component analysis (pca); attendance; school;
url https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10612
work_keys_str_mv AT juliansyahputratanjung facialrecognitionimplementationusingknnandpcafeatureextractioninattendancesystem
AT bayuanggawijaya facialrecognitionimplementationusingknnandpcafeatureextractioninattendancesystem
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