Bananas are easily damaged, improper management of bananas can result in a decrease in quality and quality. In general, to measure maturity is still done conventionally, the weakness of this method is the level of accuracy that is not consistent and prone to errors. Utilization of images is very important to determine the ripeness of bananas by utilizing digital images. With the existence of digital images, to determine the ripeness of bananas based on their color can be done computationally (technology-based), namely by applying image processing using the HSV (Hue, Saturation, Value) color space transformation method. The HSV (Hue, Saturation, Value) color model classifies the intensity components of the conveyed color information (hue and saturation) in image colors. Based on the results of research on the analysis of the maturity level of bananas, the highest training accuracy is 100% and the highest testing accuracy is 100%. Meanwhile, in the analysis of coffee bean quality, the highest training accuracy was 87.5% and the highest testing accuracy was 90%. This accuracy indicates that the method developed in this study is quite good in analyzing the level of ripeness and quality of bananas. The developed system is also made in an interface that makes it easier for users to operate. Keywords: HSV Color Space Transformation; Image processing.