<![CDATA[This study aimed to develop a mobile application for early skin cancer screening while simultaneously enhancing public awareness of skin health, with a case study conducted in the coastal area of Muara Angke, North Jakarta. The research was motivated by high levels of ultraviolet radiation exposure and limited access to healthcare services, which often lead to delayed skin cancer detection. An empirical quantitative approach was employed by utilizing a Convolutional Neural Network (CNN) based on the ResNet50 architecture, deployed through a cloud based infrastructure and integrated into a Flutter based mobile application. The system was designed using an edge cloud computing approach to address the computational limitations of mobile devices. The results indicated that the application effectively performed preliminary skin lesion screening, provided classification outcomes with confidence scores, and delivered accessible skin health education content. Although the application was not intended to serve as a medical diagnostic tool, it demonstrated significant potential to support early detection efforts and improve community level awareness of skin health. This study contributes to the advancement of mobile health (m-health) by emphasizing the practical implementation of AI based applications tailored to community needs.]]>
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