<![CDATA[The objective of this research is to investigate face identification methods in the context of employee recognition as a solution to the problem of attendance that still uses manual methods or applications without identity validation. The main goal is to achieve optimal accuracy and consistency in the identification process using Convolutional Neural Networks (CNN) with FaceNet and Haar Cascade. This research focuses on the challenge of managing employee attendance, particularly for those who are working remotely, which can be vulnerable to fraudulent activity. The proposed solution combines facial recognition to enhance identity verification, attendance tracking, and assist companies in achieving their goals. The study employed a dataset of 1,050 employee face data and divided it into three scenarios for training and testing ratios: the first scenario (80:20), the second scenario (70:30), and the third scenario (60:40). The results indicate that the model in the first scenario had the highest accuracy value of 98% and outperformed the models in the second and third scenarios in terms of precision, recall, and f1-score, with values of 98.60%, 98.70%, and 98.60%, respectively. The results indicate that the model used in the first scenario is the most effective in classifying predicted cases and consistently predicting employee identification. Based on these findings, we recommend implementing suggestions such as adding datasets and analyzing important classes to improve the accuracy and generalization of face identification models in the context of employee recognition. Combining facial recognition improves identity verification and attendance tracking, making it easier for companies to manage employee attendance with greater effectiveness.]]>
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