<![CDATA[The demand for high-quality young coconuts is growing as public awareness of their health benefits continues to rise. However, manually identifying the quality of young coconuts is often inaccurate and time-consuming. This research aims to address this issue by developing an Android-based application for detecting the quality of young coconuts, utilizing the ADDIE (Analysis, Design, Development, Implementation, Evaluation) and ISD (Instructional System Design) models. The ADDIE model serves as the framework for system development, while the ISD model helps structure the application's content and functionality. Data collection involved a comprehensive literature review of relevant references, along with testing the application on various young coconut samples. The testing process measured the accuracy of the application's quality detection compared to traditional manual methods. The results demonstrate that the developed application can detect the quality of young coconuts with high accuracy, providing a reliable tool for farmers and consumers to select high-quality produce. Beyond improving accuracy, this application is expected to enhance efficiency in quality assessment, add value to the agricultural industry, and reduce reliance on subjective manual evaluations. By streamlining the process of identifying quality young coconuts, the application has the potential to optimize their distribution and sales in the market, benefiting both producers and consumers.]]>
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