DEEP LEARNING IMPLEMENTATION FOR EMPLOYEE ATTENDANCE SYSTEM IN UNIVERSITAS PERTAMINA

Abstract

Attendance recording with RFID tags scan, make human resource (HR) staff’s task more effective and efficient because it is saving time and effort in performing manual recording and recapitulation that must be performed by the HR staff in a company. However, the number of cases where employees forget to bring their identification cards, which has an RFID tag increases the workload of human resource staff. This study proposes a facial recognition prototype as an alternative way to record employee attendance. The model used in this study uses artificial neural networks that have more than one hidden layer and uses a supervised learning approach. The results of the study show that when a high-resolution image provided for the training data, the prototype able to make an accurate prediction. However, some further study is needed before replacing existing attendance recordings with face recognition to address several problems such as distance between camera and object and accessories that affect the essential features in a face like glasses, headscarves, and mask. Further research should find the maximum distance between the object(s) and the camera and the position (angle) of the object towards the camera.