ADIVA, MUHAMMAD FATHIN (2023) FACE RECOGNITION FOR ONLINE CLASS ATTENDANCE USING DEEP LEARNING. S1 thesis, Universitas Mercu Buana.
|
Text (HAL COVER)
01 COVER.pdf Download (360kB) | Preview |
|
|
Text (ABSTRAK)
02 ABSTRAK.pdf Download (111kB) | Preview |
|
![[img]](https://repository.mercubuana.ac.id/style/images/fileicons/text.png) |
Text (BAB I)
03 BAB 1.pdf Restricted to Registered users only Download (97kB) |
|
|
Text (BAB II)
04 BAB 2.pdf Restricted to Registered users only Download (30kB) |
|
|
Text (BAB III)
05 BAB 3.pdf Restricted to Registered users only Download (495kB) |
|
|
Text (BAB IV)
06 BAB 4.pdf Restricted to Registered users only Download (99kB) |
|
|
Text (BAB V)
07 BAB 5.pdf Restricted to Registered users only Download (99kB) |
|
|
Text (DAFTAR PUSTAKA)
08 DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (168kB) |
|
|
Text (LAMPIRAN)
09 LAMPIRAN.pdf Restricted to Registered users only Download (820kB) |
Abstract
In this study, the authors present a deep learning approach for accurately recognizing faces in online class attendances. The authors evaluate the performance of our method on a dataset of online class attendees and demonstrate its ability to achieve high accuracy rates in real-world conditions. The authors approach shows promise as a reliable means of automating attendance tracking in online class settings. The face recognition system for online class attendance utilizes Dlib for feature extraction, face encoding with Python to involves extracting unique features from an individual's face and encoding them into a numerical representation, which is then compared with stored templates to accurately identify the person, face recognition using the Python library, and PIL for capturing images to accurately identify and verify students' attendance in a virtual class setting. Keywords: Face Recognition, Attendance, Capture Screen, Image Grab. Dalam penelitian ini, penulis menghadirkan pendekatan deep learning untuk pengenalan wajah secara akurat pada presensi kelas online. Penulis mengevaluasi kinerja metode kami pada kumpulan data peserta kelas online dan menunjukkan kemampuannya untuk mencapai tingkat akurasi tinggi dalam kondisi dunia nyata. Pendekatan penulis menunjukkan janji sebagai cara yang andal untuk mengotomatiskan pelacakan kehadiran dalam pengaturan kelas online. Sistem pengenalan wajah untuk kehadiran kelas online menggunakan Dlib untuk ekstraksi fitur, penyandian wajah dengan Python untuk melibatkan penggalian fitur unik dari wajah individu dan menyandikannya ke dalam representasi numerik, yang kemudian dibandingkan dengan templat tersimpan untuk mengidentifikasi orang tersebut secara akurat, pengenalan wajah menggunakan pustaka Python, dan PIL untuk mengambil gambar guna mengidentifikasi dan memverifikasi kehadiran siswa secara akurat dalam pengaturan kelas virtual. Kata Kunci: Pengenalan Wajah, Absensi, Tangkap Layar, Pengambilan Gambar.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 23 035 |
| Call Number: | SIK/15/23/030 |
| NIM/NIDN Creators: | 41519010199 |
| Uncontrolled Keywords: | Pengenalan Wajah, Absensi, Tangkap Layar, Pengambilan Gambar. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | ADELINA HASNA SETIAWATI |
| Date Deposited: | 02 May 2023 06:42 |
| Last Modified: | 02 May 2023 06:42 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/76642 |
Actions (login required)
 |
View Item |
