<![CDATA[Information about blood types is a crucial aspect that must be known in the medical field, especially in the process of blood transfusion and healthcare services. Identifying blood types is a vital step to ensure patient safety during blood transfusions. In this research, the primary focus is on blood type A+. Blood type A+ is one of the common and sought-after blood types because it can donate blood to individuals with blood types A or AB positive. Blood type A+ can receive blood from donors with blood types A or O positive. One method that can be utilized in the process of identifying blood type A+ is using digital image processing and identification methods with edge detection algorithms. The use of edge detection algorithms on an image will result in the edges of objects in that image. The goal is to highlight the details in the image and improve blurred points in vision that may arise due to errors or effects from the image acquisition process. This research aims to evaluate the capabilities of the combination of Prewitt and Canny edge detection algorithms in detecting inverted images. The image dataset used consists of 10 original images of blood type A+ and 10 inverted images. The research dataset was obtained from the IEEE DataPort website. Based on the analysis of 10 conducted experiments, the combination of Prewitt and Canny algorithms is excellent in edge detection, achieving a high accuracy level of 100%. Therefore, it can be concluded that for this issue, the combination of Prewitt and Canny algorithms is capable of identifying inverted images of blood type A+.]]>
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