[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124360-en":3,"doc-seo-124360-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124360,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",6,"Technology","FACIAL RECOGNITION SYSTEM FOR DISTANCE LEARNING STUDENT ATTENDANCE MANAGEMENT USING MACHINE LEARNING","Student attendance administration functions as a core mechanism of academic governance, shaping learning results and institutional effectiveness. The research designs a machine-learning–driven facial recognition system to improve precision and operational reliability across both on-site and remote learning settings. Facial data are collected under varying lighting, viewpoints, and distance conditions, then used through preprocessing and model training. Robust preprocessing and anti-spoofing safeguards address camera quality, illumination inconsistency, and spoofing risks. Quantitative testing reports up to 100% accuracy under controlled conditions with an average 0.8-second processing time, supporting secure, privacy-aware real-time attendance and reducing attendance fraud.","FACIAL RECOGNITION SYSTEM FOR DISTANCE LEARNING STUDENT ATTENDANCE MANAGEMENT USING MACHINE LEARNING  \nAgus Sriyanto1; Alif Sahputra1; Arif Wahyu Nugroho1; Bryan Hans Lobya1; Kusrini1*  \nMagister of Informatics1  \nUniversitas AMIKOM Yogyakarta, Yogyakarta, Indonesia 1  \n[www.amikom.ac.id](www.amikom.ac.id1)[1](www.amikom.ac.id1)  \n[agussriyanto17@students.amikom.ac.id](agussriyanto17@students.amikom.ac.id); [alifsahputra@students.amikom.ac.id](alifsahputra@students.amikom.ac.id); [arifwahyu@students.amikom.ac.id](arifwahyu@students.amikom.ac.id); [bryanhans@students.amikom.ac.id](bryanhans@students.amikom.ac.id); [kusrini@amikom.ac.id](kusrini@amikom.ac.id)*  \n(*) Corresponding Author  \n(Responsible for the Quality of Paper Content)  \nThe creation is distributed under the Creative Commons Attribution-NonCommercial 4.0 International License.  \nAbstract—The administration of student attendance constitutes a vital component of academic governance, affecting both educational outcomes and institutional efficacy. Utilizing machine learning to augment precision and efficacy, with adaptability for both physical and remote learning environments. The research methodology encompasses the acquisition of facial data from students under diverse lighting conditions, perspectives, and remote settings, succeeded by preprocessing and training of a facial recognition algorithm employing machine learning techniques. The system addresses key technical challenges such as camera quality variations, lighting inconsistencies, and spoofing risks by integrating robust image preprocessing and security safeguards. Quantitative evaluation shows that under ideal and controlled conditions, the system achieves up to 100% accuracy with an average processing time of 0.8 seconds. With the specifications Intel Core i5, RAM8 GB, minimum windows 10, NVIDIA GeForce GTX 1050, 1080p minimum camera with 30 fps frame rate, standard CMOS sensor, and automatic exposure adjustment capabilities, accuracy will drop if the conditions are not ideal. The system ensures the security and privacy of student facial because it is live with zoom or LMS. Furthermore, the incorporation of this system facilitates the realization of smart campus initiatives by delivering precise, real-time attendance information. This inquiry contributes to educational technology, enhancing operational efficacy and fostering digital transformation within higher education institutions. The designed system also seeks to reduce overall student attendance fraud.  \nKeywords: automated attendance system, attendance system, facial recognition, machine learning.  \nIntisari— Administrasi kehadiran siswa merupakan komponen penting dari tata kelola akademik, yang mempengaruhi hasil pendidikan dan kemanjuran institusional. Memanfaatkan pembelajaran mesin untuk meningkatkan presisi dan kemanjuran, dengan kemampuan beradaptasi untuk lingkungan pembelajaran fisik danjarak jauh. Metodologi penelitian mencakup akuisisi data wajah dari siswa di bawah beragam kondisi pencahayaan, perspektif, dan pengaturan jarak jauh, yang dilanjutkan dengan prapemrosesan dan pelatihanalgoritmapengenalan wajah yang menggunakan teknik pembelajaran mesin. Sistem ini mengatasi tantanganteknis utama seperti variasi kualitas kamera, inkonsistensi pencahayaan, dan risiko spoofing dengan mengintegrasikan prapemrosesan gambar yang kuat dan perlindungan keamanan. Evaluasi kuantitatif menunjukkan bahwa dalam kondisi ideal dan terkontrol, sistem mencapai akurasi hingga 100% denganwaktu pemrosesan rata-rata 0,8 detik. Dengan spesifikasi Intel Core i5, RAM8 GB, minimum windows 10, NVIDIA GeForce GTX 1050, kamera minimum 1080p dengan frame rate 30 fps, sensor CMOS standar, dankemampuan penyesuaian eksposur otomatis, akurasi akan turun jika kondisi tidak ideal. Sistem ini memastikan keamanan dan privasi wajah siswa karena disiarkan langsung dengan zoom atau LMS. Selain itu, penggabungan sistem ini memfasilitasi realisasi inisiatif kampus pintar dengan me","cbCaimRrWgpfat3o","https://ap.wps.com/l/cbCaimRrWgpfat3o","pdf",1500095,1,10,"English","en",105,"# Introduction\n## Background and motivation\n## Facial recognition in attendance management\n# Proposed System Overview\n## Data acquisition under real-world conditions\n## Preprocessing and machine learning training\n## Security safeguards (anti-spoofing)\n# Evaluation Results\n## Accuracy under controlled conditions\n## Processing time analysis\n## Impact of non-ideal environment\n# Deployment Considerations\n## Privacy and secure delivery via Zoom or LMS\n## Smart campus and fraud reduction","[{\"question\":\"How does the system support attendance management for distance learning students?\",\"answer\":\"It uses machine learning–based facial recognition to identify students and record attendance in real time, enabling both physical and remote learning scenarios.\"},{\"question\":\"What challenges does the system address during face recognition?\",\"answer\":\"It mitigates variations in camera quality, inconsistent lighting, and spoofing risks by applying robust image preprocessing and security safeguards.\"},{\"question\":\"What performance is reported in the quantitative evaluation?\",\"answer\":\"Under ideal, controlled conditions, the system achieves up to 100% accuracy with an average processing time of about 0.8 seconds, and accuracy decreases when conditions are not ideal.\"}]","FACIAL RECOGNITION SYSTEM FOR DISTANCE LEARNING STUDENT ATTENDANCE MANAGEMENT USING MACHINE LEARNING | 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does the system support attendance management for distance learning students?","Question",{"text":75,"@type":76},"It uses machine learning–based facial recognition to identify students and record attendance in real time, enabling both physical and remote learning scenarios.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenges does the system address during face recognition?",{"text":80,"@type":76},"It mitigates variations in camera quality, inconsistent lighting, and spoofing risks by applying robust image preprocessing and security safeguards.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance is reported in the quantitative evaluation?",{"text":84,"@type":76},"Under ideal, controlled conditions, the system achieves up to 100% accuracy with an average processing time of about 0.8 seconds, and accuracy decreases when conditions are not 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