Implementation of Computer Vision for Efficient Attendance and School Uniform Checking System


  • Faris Maulana Universitas Pancasila, Indonesia
  • M. Ali Akbar Sinaga Universitas Sumatera Utara, Indonesia
  • Hairul Rizal Universitas Bina Sarana Informatika, Indonesia
  • Bella Nideni Mahendra Universitas Teknologi Digital Indonesia, Indonesia
  • Lita Anggraini Universitas Teknologi Digital Indonesia, Indonesia
  • Utih Amartiwi Innopolis University, Russia


Computer Science, attendance report, uniform checking system, artificial intelligence


Managing school administrative tasks can consume substantial time and effort. Every semester, teachers find themselves occupied in repetitive manual tasks such as attendance tracking, disciplinary documentation, and assignment grading. These sometimes could take longer time than preparation for teaching. In this research, we proposed an Artificial Intelligence (AI) approach to handle this problem. In the era of Industry 4.0, AI has been managed to be personal assistance and do human repetitive tasks efficiently. However, the implementation of AI in Indonesia especially for educational institutions is still rare. Therefore, we have developed an innovative AI-driven attendance and uniform detection system by implementing computer vision models. Computer vision is a field of AI that equips machines with the ability to interpret and understand visual information from images and videos, enabling them to classify images and detect objects. The results show that computer vision has successfully facilitated swift and accurate detections for this task. We have also incorporated a timestamp to provide information about the time when students arrive at school. Subsequently, all the recorded data will be saved and organized within the school’s database. As a result, teachers are liberated from the tedium of manual data entry and can redirect their efforts toward pedagogical materials and instructional strategies.


Benotsmane, R., Kovács, G., & Dudás, L. (2019). Economic, social impacts and operation of smart factories in Industry 4.0 focusing on simulation and artificial intelligence of collaborating robots. Social Sciences, 8(5), 143.

Chen, X., Xie, H., Zou, D., & Hwang, G.J. (2020). Application and theory gaps during the rise of artificial intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002.

Cheng, C. (2022). Real-Time Mask Detection Based on SSD-MobileNetV2. 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), 761-767.

Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492.

Geitgey, Adam (2019). Machine Learning is Fun!. Self-Published.

Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Limna, P., Jakwatanatham, S., Siripipattanakul, S., Kaewpuang, P., & Sriboonruang, P. (2022). A review of artificial intelligence (AI) in education during the digital era. Advance Knowledge for Executives, 1(1), 1-9.

Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (2014). Machine Learning: An Artificial Intelligence Approach (Volume I). Elsevier.

Pandey, R., Pidlypenskyi, P., Yang, S., & Kaeser-Chen, C. (2018). Efficient 6-dof tracking of handheld objects from an egocentric viewpoint. Proceedings of the European Conference on Computer Vision (ECCV), 416-431.

Satpute, N., Bharti, N., Uikey, A., Wati, R., & Chakole, V. V. (2022). Online Classroom Attendance Marking System Using Face Recognition, Python, Computer Vision, and Digital Image Processing. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 10(2), 768-773.

Sennen, E. (2018). Mengenal Administrasi Guru di Sekolah. JIPD (Jurnal Inovasi Pendidikan Dasar), 2(1), 72-76.

Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308-6325.

Sigov, A., Ratkin, L., Ivanov, L. A., & Xu, L. D. (2022). Emerging enabling technologies for industry 4.0 and beyond. Information Systems Frontiers, 1-11.

Sophokleous, A., Christodoulou, P., Doitsidis, L., & Chatzichristofis, S. A. (2021). Computer vision meets educational robotics. Electronics, 10(6), 730.

Stockman, G., & Shapiro, L. G. (2001). Computer vision. Prentice Hall PTR.

Warsah, I., & Nuzuar, N. (2018). Analisis Inovasi Administrasi Guru dalam Meningkatkan Mutu Pembelajaran (Studi Man Rejang Lebong). Edukasi, 16(3), 262-274.

Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020). A review of object detection based on deep learning. Multimedia Tools and Applications, 79, 23729-23791.

Xu, S., Wang, J., Shou, W., Ngo, T., Sadick, A. M., & Wang, X. (2021). Computer vision techniques in construction: a critical review. Archives of Computational Methods in Engineering, 28, 3383-3397.

Zhang, Y. (2021). Image engineering. Handbook of Image Engineering, 55-83.




How to Cite

Maulana, F., Sinaga, M. A. A., Rizal, H., Mahendra, B. N., Anggraini, L., & Amartiwi, U. (2023). Implementation of Computer Vision for Efficient Attendance and School Uniform Checking System . Journal of Educational Technology and Instruction, 2(2), 80–92. Retrieved from