Sanatana Dharma, . (2025) IMPLEMENTASI ALGORITMA CNN DENGAN ARSITEKTUR RESNET-50: Untuk Presensi Kehadiran Mahasiswa FIK UPNVJ Berbasis Face Recognition. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Student attendance is a crucial component in the field of education, including at the Faculty of Computer Science, Universitas Pembangunan Nasional "Veteran" Jakarta (FIK UPNVJ). The current attendance sistems, such as Leads and manual methods, still face various challenges including attendance fraud, time consumption, and human error. To address these issues, this study proposes a face recognition-based solution utilizing a Convolutional Neural Network (CNN) algorithm with the ResNet-50 architecture. Student facial datasets were obtained through manually recorded videos and then processed using preprocessing methods such as CLAHE and image augmentation. The model was developed using transfer learning and evaluated using a confusion matrix to measure its accuracy and performance. The results show that the ResNet-50 model is capable of recognizing student faces with high performance, achieving 100% accuracy, precision, and recall on the test data. This study is expected to contribute to the development of an automated attendance sistem that improves efficiency, minimizes errors, and allows both lecturers and students to focus more on the learning process.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511013] [Pembimbing: Musthofa Galih Pradana] [Penguji 1: Radinal Setyadinsa] [Penguji 2: Jayanta] |
| Uncontrolled Keywords: | Face recognition, CNN, ResNet-50, Student Attendance |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | SANATANA DHARMA |
| Date Deposited: | 14 Aug 2025 02:53 |
| Last Modified: | 14 Aug 2025 02:53 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37373 |
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