Ripeness Level Classification of Banana Fruit Based on Hue Saturate Value (HSV) Color Space Using K-Nearest Neighbor Algorithm

Many types of bananas are cultivated locally in Indonesia, including the Muli Banana or Musa Acuminata Linn. During the post-harvest period of banana fruit, there is a problem in the sorting process of bananas based on their level of maturity. The fruit sorting process manually uses the human eye, but it is ineffective due to decreased vision and the large quantity of fruit. Therefore, we need a system that can quickly classify the ripeness of the banana fruit. This study aims to create a system that can organize the maturity level of the banana fruit. The classification system designed using the HSV color feature extraction method and the K-Nearest Neighbor classification algorithm. After going through the testing phase, the system can classify bananas into three classes: unripe, ripe, and rotten. System testing used 30 test data images, and the results show 2 test images whose classification results are wrong and 28 other test images whose classification results are correct. Based on calculations, the accuracy achieved by the system is 93.333%.