The AirNav Semarang Employee Presence System Using Face Recognition Based on Haar Cascade
DOI:
https://doi.org/10.26877/asset.v6i3.672Keywords:
Attendance System, Authentication, Face recognition, Haar Cascade ClassifierAbstract
The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented
References
Y. Komalasari, A. Anton, L. Yiharodiyah, and M. Amanda, “Radio Frequency Identification (RFID) Technology Devices in Library Services: Improving Education Services,” JMKSP (Jurnal Manajemen, Kepemimpinan, dan Supervisi Pendidikan), vol. 8, no. 2, pp. 1288–1297, 2023.
Thai-Viet Dang, “Smart Attendance System based on Improved Facial Recognition,” Journal of Robotics and Control (JRC), vol. 4, no. 1, pp. 46–53, Jan. 2023, doi: 10.18196/jrc.v4i1.16808.
D. Ma, B. Dang, S. Li, H. Zang, and X. Dong, “Implementation of computer vision technology based on artificial intelligence for medical image analysis,” International Journal of Computer Science and Information Technology, vol. 1, no. 1, pp. 70–76, 2023.
R. Singh, P. Srivastava, D. Singh, A. Srivastava, and A. S. Chauhan, “A Systematic Review Of Face Detection And Face Recognition Techniques Using Machine Learning,” Dogo Rangsang Research Journal UGC Care Group I Journal, vol. 13, no. 2, pp. 102–115, 2023.
A. I. Awad, A. Babu, E. Barka, and K. Shuaib, “AI-powered biometrics for Internet of Things security: A review and future vision,” Journal of Information Security and Applications, vol. 82, May 2024, doi: 10.1016/j.jisa.2024.103748.
D. J. Heanth and N. J. Thomas, “A Review on Vascular Biometrics for Finger Vein Authentication System,” 2024. doi: 10.3233/faia231443.
D. Palma and P. L. Montessoro, “Biometric-Based Human Recognition Systems: An Overview,” in Recent Advances in Biometrics, IntechOpen, 2022. doi: 10.5772/intechopen.101686.
F. Firmansyah, Desmira, E. Permata, and D. Aribowo, “Prototype Face Attendance System Berbasis Raspberry Pi Menggunakan Metode Eigenface di Program Studi Pendidikan Vokasional Teknik Elektro Untirta,” Reslaj: Religion Educat ion Social Laa Roiba Journal , vol. 5, no. 6, pp. 2596–2609, 2023, doi: 10.47476/as.v5i6.2331.
B. Elfitri, E. Rachmawati, and T. A. B. Wirayuda, “Prediksi Penuaan Wajah Manusia Berbasis Generative Adversarial Network,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 1, pp. 55–64, Feb. 2024, doi: 10.25126/jtiik.20241116870.
S. Modak, H. Shah, R. Surana, and D. Mishra, “A Systematic Review of Face Attendance System Based on Recognition,” Indian Journal of Research , vol. 11, no. 11, pp. 26–29, Nov. 2022, doi: 10.36106/paripex.
Aniedu A.N et al., “A Comprehensive Overview of Face detection and Face recognition Methods, Techniques and Algorithms,” International Journal of Advances in Engineering and Management (IJAEM), vol. 4, p. 116, 2022, doi: 10.35629/5252-0402116122.
R. Kosasih, “Kombinasi Metode Isomap dan KNN Pada Image Processing Untuk Pengenalan Wajah,” CESS (Journal of Computer Engineering System and Science), vol. 5, no. 2, pp. 166–170, Jul. 2020.
Munawir, L. Fitria, and M. Hermansyah, “Implementasi Face Recognition pada Absensi Kehadiran Mahasiswa Menggunakan Metode Haar Cascade Classifier,” InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan , vol. 4, no. 2, pp. 314–320, Mar. 2020, doi: 10.30743/infotekjar.v4i2.2333.
N. Sharma and G. Indrajit, “How Machine Learning Algorithms work in Face Recognition System? A Review and Comparative Study,” International Journal for Innovative Engineering and Management Research, vol. 12, no. 3, pp. 250–262, Mar. 2023, [Online]. Available: https://ssrn.com/abstract=4397531
C. N. Ihsan, “Klasifikasi Data Radar Menggunakan Algoritma Convolutional Neural Network (CNN),” Journal of Computer and Information Technology, vol. 4, no. 2, pp. 115–121, Feb. 2021.
Safwandi, Fadlisyah, and Z. A. Zulfakhmi, “Analisis Perancangan Sistem Informasi Sekolah Menengah Kejuruan 1 Gandapura Dengan Model Diagram Konteks Dan Data Flow Diagram,” Jurnal Teknologi Terapan and Sains TTS4.0, vol. 2, no. 2, pp. 535–539, 2021.
A. Setiawan, A. T. Prastowo, and D. Darwis, “Sistem Monitoring Keberadaan Suatu Mobil Berbasis GPS Dan Penyadap Suara Menggunakan Smartphone,” Jurnal Teknik dan Sistem Komputer (JTIKOM, vol. 3, no. 1, pp. 35–44, 2022.
A. Jadhav, S. Lone, S. Matey, T. Madamwar, and S. Jakhete, “Survey on Face Detection Algorithms,” Int J Innov Sci Res Technol, vol. 6, no. 2, pp. 291–297, Feb. 2021, [Online]. Available: www.ijisrt.com
L. N. Soni and Dr. Akhilesh A Waoo, “A Review of Recent Advances Methodologies for Face Detection,” 86| International Journal of Current Engineering and Technology, vol. 13, no. 2, 2023, doi: 10.14741/ijcet/v.13.2.6.
Dina M. Abdulhussein and Laith J. Saud, “A Review of Face Detection Methods Based on Feature Approach,” Iraqi Journal of Computer, Communication, Control and System Engineering, vol. 23, no. 3, pp. 1–9, Sep. 2023, doi: 10.33103/uot.ijccce.23.3.1.