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All Journal Journal of Information Systems and Informatics
Purwanto, Yudhi Setyo
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Computer Science & IT

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Real-time Multi-Screen Cheating Detection using K-Means Clustering Purwanto, Yudhi Setyo; Putra, Rakhmadi Irfansyah
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1262

Abstract

Ensuring academic honesty during online exams is becoming more and more challenging with students taking advantage of multiple screens and mirrored monitors. This research presents a privacy-sensitive, real-time multi-screen behavior detection model that does not rely on cameras or biometric sensors. The system tracks hardware and behavioral signals like screen-switch rate, focus-loss activity, idle time, and display change events. Utilizing K-Means clustering (k = 3), these metrics are segregated into three categories: Normal, Suspected, and Cheating. Implemented in Python and tested on simulated and real datasets, the model registered a silhouette score of 0.27 and showed discriminative behavior segregation through clustering analysis. Testing against a labelled dataset produced balanced accuracy of more than 80 percent, supported by confusion matrix and performance curve research. Findings show that hardware monitoring and activity-based could be an effective, camera-free means of detecting cheating in online examinations. The approach is privacy-respecting, computationally light in real time, and has understandable output for administrative exam. Drawbacks include the focus to date on Windows platforms and the need for more comprehensive cross-platform testing. Future studies will examine multimodal integration and larger scales to further increase detection accuracy and transferability.
Co-Authors Rakhmadi Irfansyah Putra