Abstract
Leaves are one part of a plant that has benefits for humans, especially for the health of the body. Leaves can be used as herbal medicine which can be an alternative that can help in increasing immunity and body resistance. However, not all leaves have medicinal properties, therefore knowledge of the types of medicinal leaves is important. The aim of this research is to develop a classification system for the image of medicinal leaves using the Extreme Learning Machine (ELM) artificial neural network model. To support the ELM algorithm, morphological feature extraction is used which can provide information about the shape characteristics of existing objects. Extreme Learning Machine (ELM) is also known as an artificial neural network approach that uses one hidden layer. At the classification stage, the Extreme Learning Machine (ELM) algorithm can determine the weight value between the input neurons and the hidden layer randomly so that the learning pattern becomes faster. Based on the results of the precision, recall and accuracy tests, the precision value is 90.67%, the recall value is 89.47% and accuracy of 90%. So, based on these results it can be said that the ELM model that was built can classify images of leaf types with medicinal properties well.
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