<![CDATA[This research aims to develop a login system using facial recognition technology on an Android application to enhance security. The main focus is to integrate Flutter and ML Kit Face Recognition, as well as to apply the Euclidean Distance method to improve authentication accuracy. The development method involves using Flutter as the framework and ML Kit Face Recognition for face recognition technology. Euclidean distance is implemented to measure the distance between facial features, allowing the system to distinguish faces more accurately. Testing was conducted with a diverse facial dataset to evaluate the performance and effectiveness of the system. The research results show that the combination of ML Kit Face Recognition and Euclidean Distance successfully enhances the security of Android applications, with the authentication system achieving a 90% accuracy rate in recognizing users' faces. This implementation has proven to be an effective and efficient solution for authentication in Android applications. The Euclidean Distance method successfully improved facial recognition accuracy significantly. In conclusion, the integration of Flutter, ML Kit Face Recognition, and Euclidean Distance offers a promising approach to Android user authentication, addressing security challenges in mobile application development. The findings of this research have implications for the development of a more secure authentication system and the improvement of the login process efficiency in Android applications..]]>
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