Aplikasi Absensi Berbasis Pangenalan wajah Multiple Person

Alexander Christian Rompas, Sherwin Sompie, Agustinus Jacobus


Abstract - The attendance system is a feature used to record the attendance of certain activities such as academic activities. The manual attendance system has drawbacks in the form of discrepancies in the absences recapitulation, loss of attendance data, and the existence of absent data manipulation. The research with the title "Implementation of Multiple Person Face Recognition for Attendance Application" aims to eliminate some of the shortcomings of manual attendance by doing automatic attendance with facial recognition. This study uses Haarcascade, DNN Method and Support Vector Maschine to be able to detect faces and recognize faces that have been inputted by the user.

Keywords – Attendance System; Deep Neural Network; Face Detection; Face Recognition; Haarcascade; Support Vector Machine;


Abstrak Sistem absensi merupakan fitur yang digunakan untuk mencatat kehadiran dari suatu kegiatan tertentu seperti kegiatan akademis. Sistem absensi manual memiliki kekurangangan berupa adanya ketidaksesuaian dalam rekapitulasi absen, kehilangan data absen, dan adanya manimpulasi data absen. Penelitian dengan judul “Aplikasi Absensi Berbasis Pengenalan Wajah Multiple Person” bertujuan untuk menghilangkan beberapa kekurangan dari absensi manual dengan cara melakukan absen secara otomatis dengan pengenanlan wajah. Penelitian ini menggunakan Haarcascade, Metode DNN dan Support Vector Machine untuk dapat mendeteksi wajah dan mengenali wajah yang telah terinput oleh user.

Kata Kunci: Deteksi Wajah; Deep Neural Network; Haarcascade; Pengenalan Wajah; Sistem Absensi; Support Vector Machine;

Full Text:


DOI: https://doi.org/10.35793/jti.16.2.2021.33453


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Published by  Informatics Engineering Study Program, Sam Ratulangi University, Manado, p-ISSN : 2301-8364 dan e-ISSN : 2685-6131