This is due to the properties possessed by these plants. There are thousands of species of medicinal plants with efficacy as efficacious herbal medicines with low side effects. Medicinal plants have different physical characteristics. Species of medicinal plants can be distinguished from the shape and color of the leaves. The main feature that becomes the dominant feature to be able to distinguish each species of medicinal plants is the feature of color and leaf shape. These features become the main key in the identification of medicinal plants. Identification is done by comparing the similarity of images of medicinal plant leaves using color and shape feature values. The similarity can be determined through differences in the content of medicinal plant leaf images, namely the value of shape and color features between the query image and the database image. The closer to zero the difference, the higher the level of similarity. The level of similarity will affect the accuracy of image recognition at the time of identification. In this study, an analysis of the accuracy of the identification of medicinal plant leaves was carried out and the computational time measurement for its identification was carried out. Improved identification accuracy by weighting the value of shape and color features. Extraction of medicinal plant leaf images uses invariant moment for shape features and color moment for color features. Preprocessing before extraction using Grayscale, resize, Histogram Equalization and Edge Enhancement. Rice image data clustering using K-Means clustering. The results showed that the identification accuracy with test data of 500 images of medicinal plant leaves reached more than 80% on a weighting scheme of Ws (weighted Shape) = 60% and Wc (weighted color) = 40% with an average computation time of less than 5 milli -second on 15 clusters.