<![CDATA[So far, cocoa farmers choose the quality of the maturity level of cocoa pods manually ormake selections based on estimates from these farmers, so that the manual method is very proneto errors in sorting the quality of cocoa pod maturity with various human factors, such as fatigueand doubt. Based on these problems, this study developed an application for classification ofcocoa pods using Hue, Saturation, Value (HSV) color extraction with the classification methodusing K-Nearest Neighbor (KNN) and applying the evaluation results method using the EuclideanDistance, so that in choosing the level of maturity Cocoa pods have the same standard and ahigher level of accuracy with digital processing. Therefore this research was conducted. Theprocess of classification of ripeness into 4 classes, namely: rotten, ripe, unripe and half ripe. Withthe KNN classification method, and the dataset used is 80 databases, as well as 40 testing data.The highest value is at k=1 with 90% accuracy, 90% precision, and 90% recall. The tool used todevelop the system is matlab.]]>
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