SisCek: A Deep Learning-Based Face Recognition System for Real-Time Exam Impersonation Detection

Authors

  • Shandy Yusril Fadlullah Faculty of Teacher Training and Education, Universitas Muhammadiyah Surakarta
    Indonesia
  • Afifah Nur Hidayah Faculty of Communication and Informatics, Universitas Muhammadiyah Surakarta
    Indonesia
  • Yuanda Eka Saputra Faculty of Teacher Training and Education, Universitas Muhammadiyah Surakarta
    Indonesia
  • Uslan Faculty of Teacher Training and Education, Universitas Muhammadiyah Kupang
    Indonesia
  • Santosa Pradana Putra Setya Negara Faculty of Formal and Applied Sciences, Universitas Muhammadiyah Madiun
    Indonesia

DOI:

https://doi.org/10.23917/saintek.v2i2.16998

Keywords:

face recognition, deep learning, impersonation detection, examination system, artificial intelligence

Abstract

The digital transformation of educational assessment systems has accelerated the adoption of computer-based technologies; however, it still faces significant challenges related to security and identity verification of examination participants. One of the major issues is impersonation, where unauthorized individuals act as proxies during exams, thereby compromising academic integrity. This study aims to develop and evaluate SisCek (Sistem Pendeteksi Calo Ujian/ Exam Broker Detection System) based on face recognition and deep learning as a solution to automatically and in real time detect and prevent such practices. The research employs an experimental approach involving facial data collection, preprocessing, model training using a Convolutional Neural Network (CNN), and integrated system implementation. The evaluation is conducted using accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR), as well as testing under real examination scenarios. The results show that the proposed model achieves an accuracy of 96.8%, with a FAR of 2.1% and an FRR of 3.4%. System-level testing demonstrates a detection success rate of 96% for both legitimate participants and impostors, with an average response time of 2.5 seconds, satisfying real-time system requirements. Comparative analysis indicates that SisCek outperforms conventional systems and previous studies, particularly in real-time impersonation detection and full integration with examination systems. This study provides a significant contribution to the development of AI-based examination security systems and has strong potential to enhance the integrity, fairness, and credibility of educational assessment in the digital era.

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Submitted

2026-04-14

Accepted

2026-04-18

Published

2026-04-18

How to Cite

Fadlullah, S. Y., Hidayah, A. N., Saputra, Y. E., Uslan, & Negara, S. P. P. S. (2026). SisCek: A Deep Learning-Based Face Recognition System for Real-Time Exam Impersonation Detection. Jurnal Penelitian Sains Teknologi, 2(2), 149–164. https://doi.org/10.23917/saintek.v2i2.16998

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Articles