<![CDATA[Tomatoes and chili peppers are essential commodities in the agricultural and food industry, playing a crucial role in nutritional diversity and flavor in human diets. Identifying the ripeness of these fruits is a critical step in the food supply chain, yet it is often done manually by directly observing the ripeness of chilies and tomatoes, which is time-consuming and susceptible to observer subjectivity. Therefore, a system that can identify the ripeness of tomatoes and chili peppers is needed. This system implements the HSI color space extraction method and the K-NN method. K-NN can classify plants based on colors extracted using the HSI color space, which includes three dimensions: Hue (H), Saturation (S), and Intensity (I). The research results in a model from the tomato and chili pepper dataset with an accuracy of 92% and a data split ratio of 80%:20%. This model is implemented in web and mobile formats, expected to efficiently and accurately identify the ripeness of tomatoes and chili peppers. This can help farmers determine the optimal harvest time, improve agricultural production and quality, and provide more reliable information in the food supply chain]]>
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