<![CDATA[This research aims to develop a method for identifying the ripeness level of papaya based on color features using the Support Vector Machine (SVM) algorithm. In the introduction, it is emphasized that generally, the color changes in papaya skin serve as the primary indicator of ripeness, but the accuracy of human observations in distinguishing colors can sometimes be suboptimal. Therefore, this study focuses on utilizing the SVM algorithm, particularly recognized for its excellent classification capabilities, especially in image processing and classification.The initial step in the research method involves a literature review to gather the latest information on fruit ripeness classification, with a specific emphasis on color features. The subsequent steps include formulating problems and hypotheses to determine whether color-based classification methods, particularly SVM, can effectively classify papaya ripeness levels. The design and implementation phase encompass capturing papaya images using a smartphone camera, converting the images from RGB to LAB, and extracting color features using a multi-level SVM. Testing and evaluation are then conducted to assess the system's accuracy.The implementation results indicate an accuracy rate of 96%, categorizing papayas into three classifications: mature, partially mature, and immature. Evaluation metrics such as precision, recall, and F1-score provide in-depth insights into the system's performance, demonstrating SVM's capability in identifying papaya ripeness levels. In conclusion, this research successfully applies SVM as an effective method for classifying papaya ripeness based on color features, contributing to the development of an accurate and reliable automated system for fruit ripeness identification.]]>
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