<![CDATA[Employee attendance is one of the important aspects of company management. However, the manual attendance process often causes issues such as queues, inaccurate record-keeping, and potential fraud. Therefore, this study aims to develop an automated attendance system based on facial recognition by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) method, using the Python programming language.This system is designed to automatically detect and recognize employees' faces through a camera, so that the attendance process can be carried out more quickly, efficiently, and with minimal contact. The model training process is conducted by collecting employee facial data, which is then processed into embeddings using CNN. Subsequently, the facial detection results are matched with the data stored in the database.This study was conducted at Aris Motor company as the trial location for the system. The test results showed that the system was capable of recognizing This research was conducted at Aris Motor company as the trial location for the system. The test results showed that the system was able to recognize employees' faces with a good level of accuracy and provide feedback such as “Welcome [name]” when they first check in, as well as detect if a face has previously checked in or is not recognized at all.With this system, it is expected that the attendance process at Aris Motor will become more modern, practical, and reliable, while also serving as an initial step towards the digitalization of attendance systems in the workplace.Keywords: Automatic Attendance, Face Detection, Deep Learning, CNN, Python, Aris Motor]]>
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