Gladiola, Dita Arinda (2019) RANCANG BANGUN SISTEM PRESENSI MAHASISWA BERBASIS PENGENALAN CITRA WAJAH MENGGUNAKAN METODE DEEP LEARNING. Tugas Akhir thesis, University of Technology Yogyakarta.
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12. ABSTRAK_5150711023_Dita Arinda Gladiola_Teknik Elektro done.pdf Download (90kB) | Preview |
Abstract
ABSTRACT Student presence is one of the most important elements in lecturing activities. With a presence system that still uses a manual system, the process of recording student attendance can cause several problems. One of them is students often take advantage of gaps and collaborate with other students to commit fraud, for example students who are present in class, often sign the presence of their friends who are not present in lectures. The problem faced in this study is how the system can minimize cheating using face images for the presence of each student with the appropriate dataset. This study uses the MTCNN (Multi-Task Cascade Convolutional Network) method for the process of detecting and recognizing facial patterns during student presence. The system is built into three stages, namely the first stage in the form of student photo dataset training. The second stage is facial recognition during the presence process using a webcam camera to detect the image of a student's face during presence. Then testing the student photo is taken. If a face is detected and recognized then the captured image of the student will bring up the Student Number that matches the dataset. The third stage is the transfer of facial recognition results in the form of NIMs into the database system and displayed on the web. The results of testing the accuracy of face detection obtained an average percentage of accuracy is 100% for all photo images that have met the requirements.. Keywords: Face Image, MTCNN (Multi-Task Cascade Convolutional Network), Deep Learning, Dataset, Website.
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Teknik Elektro |
| Depositing User: | Kaprodi Teknik Elektro |
| Date Deposited: | 17 Oct 2019 04:37 |
| Last Modified: | 17 Oct 2019 04:37 |
| URI: | http://eprints.uty.ac.id/id/eprint/3494 |
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