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STMIK YMI Tegal
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Application of nearest neighbor interpolation method and naïve bayes classifier for identification of bespectacled faces Setiawan, Dodi; Gunawan, Gunawan; Zaenul Arif
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.213

Abstract

Facial identification has become necessary in the era of advanced technology, especially in security and human-computer interaction. However, accessories such as glasses often complicate the identification process. This research aims to develop a facial identification system that can recognize bespectacled individuals with high accuracy, overcoming the limitations of conventional facial recognition technology. The method combines nearest neighbor interpolation to improve image quality and Naïve Bayes classification to distinguish between bespectacled and non-spectacled faces. The results showed that the developed model effectively identified bespectacled subjects with a high recall rate, although accuracy and precision still needed improvement. The implications of this research are significant for the field of biometric security and facial recognition, offering new solutions for more inclusive and adaptive facial recognition systems and opening up opportunities for further research in method optimization and dataset quality improvement.
Application of the viola-jones algorithm method to recognize faces of Stmik Tegal students Azmi, Muchamad Nauval; Nugroho, Bangkit Indarmawan; Septiana, Pingky; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.214

Abstract

This study examines the application of the modified Viola-Jones algorithm for student facial recognition at STMIK YMI Tegal, aiming to improve the efficiency and safety of the student attendance system. By adapting the algorithm to address the challenge of facial recognition accuracy from different angles and lighting conditions, a quasi-experimental quantitative design involved collecting data through photographic sessions with student subjects, followed by preprocessing to improve the quality of the analysis. The modification was evaluated for its ability to handle variations in facial and lighting conditions, showing significant improvements with 60% accuracy and precision, recall, and an F1-score of 71.43%. These findings demonstrate the effectiveness of the modification in improving facial recognition, potentially contributing significantly to attendance management and safety practices in educational settings. This research not only strengthens the existing literature.
Co-Authors Aang Alim Murtopo Ahmad Zulfikri Aimar Akbar, Aminnur Aisyach Aminarti Santoso Alan Eka Prayoga Ali Murtopo, Aang Anandianshka, Sawaviyya Anandianskha, Sawaviyya Andriani, Wresti Arif, Zaenul Azmi, Muchamad Nauval Bangkit Indarmawan Nugroho Catur Supriyanto Dodi Setiawan Dodi Setiawan Dwi Fina Fahirah Fadila, Nurul Firmansyah, Akhmad Lutfi Hafid Subechi, Fadlan Handayani, Sri Intan Mayla Faiza Intan Mayla Faiza Khadziqul Humam Munfi Kurniawan, Rifki Dwi Miftakhudin, Muhammad Moonap, Dinar Auranisa Muchamad Nauval Azmi Muh Ridwan Nugroho Adhi Santoso Nur Aisyah Nurokhman, Akhmad Nurul Fadhilah Purwanto Purwanto Rifki Dwi Kurniawan Riyadi, Fajar Sugeng Santoso, Bayu Aji Santoso, Nugroho Adhi Sarif Surorejo Sawaviyya Anandianskha Sawaviyya Anandianskha Sawavyya Anandianskha Septian Ari Wibowo Septiana, Pingky Sya’bani, Adhita Zulfa Syefudin, Syefudin Wilda Shabrina Wresti Andriani Wresti Andriani Wresti Andriani Yan Kurniawan Zaenul Arif Zain, Ahmad Muzakky