<![CDATA[Banana horn (Musa corniculata) is widely used as a food ingredient. In general, bananas are suitable to be processed into food group dishes. Bananas are very much needed in the domestic market as well as the international market. Therefore, to increase the selling value of horn bananas, quality standards must be maintained. Based on this problem, a system for classifying ripe bananas was carried out using RGB and HSV colors and Convolutional Neural Network (CNN). Classifying determines image processing using matlab software and the preparation of the classification system is divided into 4 classes, namely raw, half cooked, ripe and overcooked. The results of the study found that it was necessary to improve the quality standard of horn bananas. And the data carried out in this study contained 634 data divided into 506 training data and 128 test data. From this study, the accuracy obtained is 89% with more classification results of test data, namely 128 by obtaining accurate classification results as many as 114 and 14 data obtaining inaccurate classification results. Keywords— Classification, Banana horn, HSV, Matlab, Image Processing]]>
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