<![CDATA[Wild plants or what are usually called weeds are plants that are considered harmful because they grow in unwanted places. But it turns out that some wild plants have many benefits for the health of the human body. Wild plants have many forms of vegetation, one of which is often encountered is shrubs. There are many wild herbaceous plants that are efficacious as medicine. However, most of the people who do not have knowledge about the types of wild shrubs that have medicinal properties. This study aims to implement the Radial Basis Function (RBF) algorithm for the classification of wild herbaceous plant species with medicinal properties by extracting color and texture features. The color feature extraction is based on the average RGB value, while the texture feature extraction uses a Gabor filter with the mean, entropy, and variance parameters of the magnitude image. The result of feature extraction becomes input data which will be managed by the RBF artificial neural network. RBF is a neural network that has three layers that have feedforward properties that can assist in solving classification or pattern recognition problems. Based on the test results, the precision value is 91%, recall is 89% and accuracy is 90%. These results show that the Radial Basis Function (RBF) algorithm with color and texture feature extraction can classify wild shrubs with medicinal properties well.]]>
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