Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA)

Suhery, Cucu; Ruslianto, Ikhwan

doi:10.26418/jp.v3i1.19792

Cited by 11 publications

(10 citation statements)

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“…Beberapa penelitian sebelumnya [6,7], memberikan solusi presensi dengan menghadirkan sistem pengenalan pola wajah berbasis mobile. Alternatif ini cukup efektif untuk diintegrasikan dengan sistem utama pada sebuah institusi, di samping penghematan penyediaan alat presensi.…”

Section: Pendahuluanunclassified

Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning

Miftakhurrokhmat

Rajagede²,

Rahmadi³

2021

RESTI

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Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students. Sometimes there are still fraudulent practices by students to achieve minimum attendance. From the administrative perspective, a paper-based presence system is potentially wasteful and extends the administrative stage because it requires manual recapitulation. This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi. The method used in this research is a deep learning approach with CNN based architecture, FaceNet, to recognize faces. In addition to facial images, the system will also validate the attendance with location and time data. Location data is obtained from matching SSID from the database, and time data is taken when the user sends attendance data through API. This attendance system consists of three applications: web, mobile, and services installed on a mini-computer, which are integrated to sending attendance data to the academic system automatically. As confirmation, students are required to smile selfies to strengthen the validity of their presence. The testing model's accuracy results are 92.6%, while for live testing accuracy the model obtained 66.7%.

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Section: Pendahuluanunclassified

Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning

Miftakhurrokhmat

Rajagede²,

Rahmadi³

2021

RESTI

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“…Pada sisi lain berbagai sistem monitoring data presensi yang telah ada saat ini memiliki kekurangan dan kelebihan masing-masing, dan perlu untuk terus dikembangkan sehingga memudahkan dalam proses pengolahan datanya [2]. Mekanisme pertama masih berupa kertas fisik yang rentan rusak, membutuhkan waktu yang relatif lama dan sulit dalam mengkalkulasi datanya.…”

Section: Pendahuluanunclassified

Penerapan Face Recognition Pada Sistem Presensi

Ramdhon

Febriya

2021

JACOST

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The development of digital image technology is increasing nowadays. However, the use of image technology on surveillance cameras has not been optimally utilized. On the other hand, the various presence data monitoring systems that currently exist have their respective advantages and disadvantages, and need to be continuously developed so as to facilitate the data processing. The student attendance system at STT Bandung is basically good but it is still not optimal. The process of collecting student attendance data is still quite time-consuming and still allows human errors to occur in the data input process. Therefore, the author intends to help overcome this by utilizing face recognition technology in an integrated presence process. LBPH (Local Binary Pattern Histogram) is currently the best method in face recognition technology because the detection and recognition process is relatively fast and has the highest level of accuracy when compared to other methods. After testing the resilience of the system from the results of the prototyping that was built, the results obtained with a success rate of 86.85%.

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“…Perkembangan sistem presensi saat ini sudah mulai merambah melalui identifikasi wajah yang dilakukan oleh kamera di dalam ruangan yang berfungsi untuk mengidentifikasi dan mengetahui posisi wajah [3]. Pemanfaatan kamera digital dilakukan untuk mengetahui estimasi posisi atau menentukan letak lokasi suatu objek dalam suatu ruang yang terjangkau pandangan kamera sehingga dapat mengenali koordinat lokasi objek [4].…”

Section: Pendahuluanunclassified

“…OpenCV (Open Computer Vision Library) merupakan open source library yang memiliki lebih dari 500 algoritma dan telah dioptimasi serta disediakan untuk menangani permasalahan mengenai computer vision dan machine learning [3].…”

Section: E Opencvunclassified

Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa

Pratama¹,

Prasetiadi²,

Usada³

2020

JuTISI

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The current presence system can be done with a computerized system, one of which is the face biometric system. This study focuses on the application of position estimation and tracking based on clustering on people's faces to determine the position in three dimensions. Position estimation can be obtained by making a kernel that is ready to be used to predict three-dimensional coordinates of faces based on two-dimensional coordinates of two images. Position estimation can be done by utilizing the Machine Learning algorithm family. In this study, Least Absolute Shrinkage and Selection Operators (LASSO) is used to perform the position estimation. Meanwhile, clustering in this study uses the K-Means algorithm. Based on the test results, the kernel error obtained in estimating the face location is 9.23 cm. The tracking accuracy of an object based on clustering is 100%.

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Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA)

Cited by 11 publications

References 0 publications

Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning

Presensi Kelas Berbasis Pola Wajah, Senyum dan Wi-Fi Terdekat dengan Deep Learning

Penerapan Face Recognition Pada Sistem Presensi

Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa

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