Abstract— The manual assessment of rambutan fruit maturity presents challenges in terms of efficiency and accuracy, especially in large-scale fruit processing operations. This study addresses the limitations of the manual system by proposing an automated classification system for rambutan maturity, employing image processing and machine learning techniques. The inefficiencies in the manual assessment process become apparent, motivating the development of an automated solution. A dataset containing images of rambutans at various ripening stages is collected, and preprocessing methods are implemented to improve image quality. Through machine learning, a classification model is trained on a labeled dataset. The results of the proposed system showcase its effectiveness in accurately categorizing rambutans into distinct maturity levels. This automated approach not only overcomes the challenges of the manual system but also has the potential to enhance efficiency and precision in the fruit sorting process, offering valuable insights for the agricultural and food processing industries