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All Journal Jurnal Simetris Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Komputer AMIK BSI Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) Jurnal Sains dan Informatika Jurnal Komtika (Komputasi dan Informatika) Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Zero : Jurnal Sains, Matematika, dan Terapan JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Tekinkom (Teknik Informasi dan Komputer) Infotek : Jurnal Informatika dan Teknologi Jurnal Sistem Informasi Journal of Students‘ Research in Computer Science (JSRCS) Informatics and Computer Engineering Journal Jurnal Sistem Informasi dan Teknologi (SINTEK) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Aruna Mengabdi (ARMI) Journal of Accounting Information System Sintek Kuwera Jurnal Komtika (Komputasi dan Informatika) Jurnal Teknoinfo
Mely Mailasari
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Optimization of Real-Time Object Detection in Viola-Jones Method with Enhanced AdaBoost Sucitra Sahara; Rizqi Agung Permana; Mely Mailasari
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.27876

Abstract

Face recognition is a widely used biometric technology in security systems, automated attendance, and surveillance applications. This study proposes an enhanced real-time face detection method by integrating a modified AdaBoost-based feature selection strategy into the Viola–Jones framework. The applied mathematical contribution of this study lies in formulating the optimization process as an empirical risk minimization model with adaptive boosting weight updates to reduce face recognition error. The proposed approach optimizes the weighting of weak classifiers by prioritizing Haar-like features with minimal weighted classification error at each boosting iteration, thereby improving discriminative capability. Experiments were conducted on a camera-based dataset consisting of face and non-face samples under varying illumination and pose conditions. Prior to optimization, the system achieved a precision of 70.04% and a recall of 70.05%. After applying the proposed enhancement, precision increased to 81.04% and recall to 90.02%. These results demonstrate that the modified AdaBoost integration significantly improves detection accuracy while remaining suitable for real-time face detection applications.
Co-Authors Ahmad Setiadi Annida Purnamawati Annida Purnamawati Ari Puspita Azriel Yudha Pratama Dewantara, Satria Bima Erma Delima Sikumbang Hidayanti Murtina Khoirun Nisa Linda Marlinda Monikka Nur Winnarto Purnamawati, Annida Rizqi Agung Permana Rohmat Rifai Sucitra Sahara Widyastuti, Reny Winarto, Monikka Nur Winnarto, Monikka Nur