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All Journal Jurnal Simantec Software Development Digital Business Intelligence and Computer Engineering
Gunawan, Ellexia Leonie
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Optimization of facial recognition authentication system using InceptionResNetV1 with Pretrained VGGFACE2 Gunawan, Ellexia Leonie; Mas Diyasa, I Gede Susrama; Jauharis Saputra, Wahyu Syaifullah
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29776

Abstract

Face recognition as a biometric authentication method continues to evolve due to its high security and ease of use. However, training models from scratch faces challenges such as the need for large datasets and high computational resources. This study aims to optimize the face authentication system using the InceptionResNetV1 architecture with a transfer learning approach from the pretrained VGGFace2 model and to compare its performance with CASIA-WebFace. Face detection is conducted using YOLOv8, face embeddings are generated by InceptionResNetV1, and authentication is performed by calculating the Euclidean distance between embeddings. Face data were collected from university students and divided into training and testing datasets. Performance evaluation includes accuracy, precision, recall, F1-score, and the confusion matrix. The results show that the VGGFace2 model achieved an accuracy of 98.75%, a recall of 100%, and an F1-score of 99.26%, with no False Negatives, while CASIA-WebFace achieved an accuracy of 86.25% with a recall of 85.07%. The main contribution of this study is to demonstrate that the use of transfer learning with the pretrained VGGFace2 model can significantly improve the accuracy of face authentication systems and to show its effectiveness for developing systems with limited data and computational resources. This study contributes by highlighting the superiority of the pretrained VGGFace2 model in face authentication systems and emphasizing the effectiveness of transfer learning for implementing accurate systems under resource constraints.Keywords: Authentication System, InceptionResNetV1, Face Recognition, Transfer Learning, VGGFace2
Analisis Video Keluhan Pelanggan Menggunakan Automatic Speech Recognition dan Analisis Polaritas Sentimen Fahrudin, Tresna Maulana; Aryananda, Rangga Laksana; Gunawan, Ellexia Leonie; Belardo, Valentino; Marcelia, Firsta; Halim, Christina
Software Development, Digital Business Intelligence, and Computer Engineering Vol. 1 No. 1 (2022): SESSION (SEPTEMBER)
Publisher : Politeknik Negeri Banyuwangi Jl. Raya Jember km. 13 Labanasem, Kabat, Banyuwangi, Jawa Timur (68461) Telp. (0333) 636780

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57203/session.v1i1.2022.14-21

Abstract

Tingkat kepuasan pelayanan pelanggan dapat ditinjau berdasarkan keluhan-keluhan pelanggan. Begitu besarnya potensi transaksi penjualan produk dengan pelanggan melalui e-commerce juga meningkatkan peluang terjadinya komplain atau keluhan pelanggan terkait kecacatan produk, keterlambatan produk, kualitas produk, dan lainnya. Keluhan pelanggan biasa disampaikan melalui ulasan-ulasan di media sosial berbentuk teks. Namun, data keluhan pelanggan saat ini semakin bervariasi dalam bentuk video. Oleh karena itu, penelitian ini mencoba untuk menganalisis video keluhan pelanggan menggunakan automatic speech recognition dan analisis polaritas sentimen. Hasil eksperimen menunjukkan bahwa telah ditemukan beberapa keluhan pelanggan pada video yang dianimasikan bertempat di restoran dan mini market. Nilai compound pada video keluhan pelanggan di restoran pada potongan video ke-7 sebesar -0.4747, potongan video ke-10 sebesar -0.8664, dan potongan video ke-11 sebesar -0.6801, sedangkan nilai compound pada video keluhan pelanggan di mini market pada potongan video ke-1 sebesar -0.1027, potongan video ke-2 sebesar -0.2023, dan potongan video ke-5 sebesar -0.5563. Nilai compound tersebut merepresentasikan keluhan pelanggan yang mengarah ke sentimen negatif.
Co-Authors Aryananda, Rangga Laksana Belardo, Valentino Fahrudin, Tresna Maulana Halim, Christina Jauharis Saputra, Wahyu Syaifullah Marcelia, Firsta Mas Diyasa, I Gede Susrama