<![CDATA[Bananas are fruits that are rich in vitamins, minerals, and carbohydrates. Banana trees are often cultivated as they have many benefits. In growing banana trees, it is necessary to consider the ripeness level of bananas since it can determine the quality of bananas when harvested. The ripeness level of bananas is related to marketing reach. If the marketing reach is far, the banana should be harvested when it is still raw. Therefore, a system that can classify bananas’ ripeness levels is needed. In this study, 45 banana images were collected, with a composition of 30 images as training data and 15 images as test data. Afterwards, the texture feature extraction method was utilized to determine the parameters affecting the ripeness level of bananas. The texture feature extraction used was based on a histogram that generated several parameters i.e., average intensity, skewness, energy descriptor, and smoothness in the image. In the subsequent stage, the classification based on the features obtained using KNN algorithm was conducted. Based on the results, it was found that the classification accuracy rate was 88.89%.]]>
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