IMPLEMENTASI REST API GOLANG PADA SISTEM PRESENSI WAJAH BERBASIS FACE EMBEDDING ARCFACE

Septian Dwiky Hermawan, Landung Sudarmana, Gustina Sapriani

Abstract


Conventional attendance systems carry a high risk of data manipulation, necessitating more secure biometric technology. This study aims to implement a facial biometric attendance system using a REST API architecture with the Golang programming language. The research method used is applied research, while system development follows the Waterfall model. The facial recognition process is carried out by extracting image features into a numerical representation, a face embedding (a 512-dimensional vector), using the ArcFace Convolutional Neural Network (CNN) model. The embedding data is stored in a PostgreSQL database using the pgvector extension to support cosine similarity search. Test results show that the system is capable of accurate identity verification. With a threshold of 0.43, the system achieved an accuracy of 78.04%, a False Acceptance Rate (FAR) of 0.00%, and a False Rejection Rate (FRR) of 23.15%. This demonstrates the effectiveness of using face embedding in a Golang REST API service for providing a secure, real-time attendance solution.

Sistem presensi konvensional memiliki risiko manipulasi data yang tinggi, sehingga diperlukan teknologi biometrik yang lebih aman. Penelitian ini bertujuan mengimplementasikan sistem presensi biometrik wajah menggunakan arsitektur REST API dengan bahasa pemrograman Golang. Metode penelitian yang digunakan adalah penelitian terapan (applied research), sedangkan pengembangan sistem dalam penelitian ini mengikuti model Waterfall. Proses pengenalan wajah dilakukan dengan mengekstraksi fitur citra menjadi representasi numerik berupa face embedding (vektor 512-dimensi) melalui model Convolutional Neural Network (CNN) ArcFace. Data embedding disimpan dalam basis data PostgreSQL menggunakan ekstensi pgvector untuk mendukung pencarian kemiripan berbasis cosine similarity. Hasil pengujian menunjukkan bahwa sistem mampu melakukan verifikasi identitas secara akurat. Dengan nilai ambang batas (threshold) 0,43, sistem memperoleh nilai akurasi 78,04% dengan False Acceptance Rate (FAR) 0,00% dan False Rejection Rate (FRR) 23,15%. Hal ini membuktikan bahwa penggunaan face embedding pada layanan REST API Golang efektif dalam memberikan solusi presensi yang aman dan real-time


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DOI: https://doi.org/10.56486/jris.vol6no2.1208

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