Diabetes is a significant and continuously rising health challenge in Indonesia. Although many artificial intelligence (AI)-based health applications have been developed for early detection, most function as "black boxes," lacking transparency in their predictions. Explainable AI (XAI) methods offer a solution, yet their technical outputs are often incomprehensible to non-expert users. This research aims to develop a mobile application front-end that presents XAI-driven diabetes risk analysis in an intuitive, understandable format. Development followed the waterfall methodology, comprising requirements analysis, interface design, implementation, and evaluation. Based on user preference surveys, the application adopts two primary visualization types - bar charts and pie charts - to convey the contribution of each risk factor. These are complemented by personalized textual narratives generated via integration with GPT-4o. The application was developed natively for Android using Kotlin and Jetpack Compose. The resulting prototype interprets SHAP (SHapley Additive exPlanations), a key XAI approach, into accessible graphical visualizations and narratives. Evaluation through user comprehension testing (Likert scale and interviews) and technical functionality testing confirmed the research objectives were met. The combination of visualization and textual narrative effectively enhanced user understanding (average score 4.31/5) and empowered preventive action, supported by a 100% technical testing success rate.

翻译：糖尿病是印度尼西亚一项重大且持续上升的健康挑战。尽管已开发出许多基于人工智能（AI）的健康应用用于早期检测，但大多数功能如同"黑箱"，其预测缺乏透明度。可解释人工智能（XAI）方法提供了一种解决方案，但其技术输出对于非专业用户而言往往难以理解。本研究旨在开发一个移动应用前端，以直观、易懂的格式呈现XAI驱动的糖尿病风险分析。开发遵循瀑布式方法，包括需求分析、界面设计、实施与评估。基于用户偏好调查，该应用采用两种主要的可视化类型——条形图和饼图——来传达每个风险因素的贡献度。这些可视化辅以通过与GPT-4o集成生成的个性化文本叙述。该应用使用Kotlin和Jetpack Compose为Android平台进行原生开发。最终的原型将SHAP（SHapley Additive exPlanations）这一关键的XAI方法解释为易于理解的图形可视化和叙述。通过用户理解度测试（李克特量表和访谈）和技术功能测试进行的评估证实了研究目标的达成。可视化与文本叙述的结合有效提升了用户理解度（平均得分4.31/5）并促进了预防性行动，其支持依据是100%的技术测试成功率。