DEVELOPMENT OF A PRESENCE SYSTEM WITH FACE RECOGNITION INTEGRATED WITH ONLINE APPLICATIONS USING DEEP LEARNING METHODS TO EXPAND THE TEACHING AND LEARNING PROCESS AT SMKN 9 MALANG - Fullchis Nurtjahjani; Galih Putra Riatma; Kadek Suarjuna Batubulan; Ane Fany Novitasari (CV. RADJA PUBLIKA, 2024)
Attendance systems are needed in various fields such as companies, government agencies, educational agencies, and others. Especially for educational institutions, the attendance system functions to control or determine the presence of students, teaching staff, and educational staff. SMKN 9 Malang City is an educational institution that has the obligation and role to equip its graduates with life skills in an integrative manner, which combines generic and specific potential to solve and overcome life's problems. This school has 5 expertise concentrations from which students can choose. In measuring the presence of the academic community, SMKN 9 Malang City uses an attendance system that includes Finger (for students and PTT and GTT) and the application from the East Java Province BKD e-Presence ASN. However, in its use several weaknesses were found that prevented the application from running efficiently, these weaknesses were 1) the number of locations, 2) a large number of students, 3) Time was less effective because the ratio between tools and students was still not ideal, 4) Sometimes some students have to try several times for less sensitive fingerprints, 5) Not connected with parents so that the school, the students' guardians/committee collaborate in monitoring their son. To follow up on problems with the attendance system used, schools need a more effective and efficient attendance system, namely by using Face Recognition Integrated with Online Applications Using Deep Learning Methods. The system created can make attendance easier for students, teaching staff, and educational staff. It is hoped that this system can improve student discipline and make it easier to monitor the performance of teaching staff and educational staff. Keywords: Face Recognition, Deep Learning, Attendance