<![CDATA[A mask is a type of personal protective equipment (PPE) that is essential for protecting the nose and mouth from contamination by droplets or airborne particles. The use of masks became highly popular during the Covid-19 pandemic, which began in December 2019 in China and peaked in Indonesia in 2020. Despite the pandemic subsiding and vaccinations increasing immunity, some companies still require masks to prevent the spread of illnesses such as colds and flu, especially in work processes that produce smoke, such as soldering and welding. To ensure employees comply with mask usage, effective supervision is necessary. Manual supervision is less efficient, thus a digital detection method is needed. This study developed a mask detection application using deep learning algorithms and the TensorFlow Lite framework on an Android platform. The application can detect mask usage with 100% accuracy at a distance of 1 to 5 meters. The system was tested under various lighting conditions and environments to ensure reliability. Additionally, the implementation of this technology can be extended to other public areas to ensure compliance with health protocols. This tool helps companies easily monitor and enforce mask-wearing discipline among employees, thereby enhancing workplace safety and health. Future work could explore the integration of this system with other health monitoring tools to create a comprehensive safety solution.]]>
