Design of Attendance and Room Monitoring Application with Face Recognition using Convolutional Neural Network method

Abstract

The development of technology in the field of facial recognition provides a great opportunity to improve efficiency and security in various aspects, one of which is the attendance and room surveillance system. This study aims to design an attendance and room surveillance application based on facial recognition using the Convolutional Neural Network (CNN) method in a private company engaged in the property sector. This application is designed to simplify the employee attendance process and improve room surveillance by automatically recognizing employee faces, thereby reducing the risk of attendance fraud and ensuring more accurate attendance. The CNN method was chosen because of its ability to process images and recognize facial patterns with high accuracy. This system consists of several main features, namely employee face registration, automatic face-based attendance, and monitoring employee presence in the office space. The test results show that this application is able to identify faces with a good level of accuracy, as well as provide convenience and comfort for users.