Article Per Year (5 Year)
p-Index From 2020 - 2025
1.116
P-Index
This Author published in this journals
All Journal Jurnal Pranata Hukum JURNAL EDUCATION AND DEVELOPMENT Progressive Law Review G-Tech : Jurnal Teknologi Terapan SASI
Prastyo, Agung Budi
Fakultas Hukum Universitas Lampung, Bandar Lampung
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : G-Tech : Jurnal Teknologi Terapan

 1 
Analisis Metode CNN menggunakan Arsitektur Facenet dan VGG16 dalam Mengenali Wajah Penyandang Tunanetra Prastyo, Agung Budi; Setyanto, Arief
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6573

Abstract

Face recognition is a rapidly growing biometric technology, especially with the application of Convolutional Neural Networks (CNN) such as FaceNet and VGG16. This research aims to evaluate the effectiveness of both CNN models in recognizing the faces of visually impaired people, who face the challenge of limited vision in image retrieval. The research uses two face detection methods, namely MTCNN and HaarCascade, to analyze the effect of face detection on recognition accuracy. The experimental method was conducted by collecting facial data of visually impaired people under various lighting conditions and expressions. The results show that accurate face detection greatly affects the performance of face recognition models, with MTCNN providing better face detection results (93.75% detection accuracy) than HaarCascade (83.75% detection accuracy). Both models, FaceNet and VGG16, show excellent face recognition accuracy (100%) if the face image is correctly detected by MTCNN. Therefore, for face recognition of visually impaired people, it is recommended to use MTCNN as the face detection method, followed by FaceNet or VGG16 for face recognition.
Co-Authors Ahmad Rifai Arief Setyanto Dauri Dauri Okta Ainita Ricco Andreas Rifka Yudhi Rodhi Agung Saputra Satria Prayoga Syamsu, Syamsir