Study and Research on the Identification of the Leaves of Indonesian Herbal Medicines Using Manhattan Distance and Neural Network Algorithms

Abstract

Indonesia is one country that has enormous potential in the use of medicinal plants as herbal medicines. Utilization or use of plants, especially medicinal plants as a means of healing disease has long been used. However, people in general still have difficulty knowing the types of plants that can be used as herbal medicines. This is due to the limited information and knowledge possessed by the community to identify and identify the use of medicinal plants. This study describes the development of feature extraction in leaf images for the identification of medicinal plants, where the main difficulty in the leaf identification stage is that the morphological (physical leaf shape) and physiological (leaf shape characteristics) are different for each type of leaf. There are three methods proposed in this research, namely the first is the proposed leaf feature model in the form of 16 perimeter point distances to leaf centroid points and seven median line connectors. The second is to develop leaf feature extraction methods and algorithms so that 23 leaf shape features can be generated for each type of medicinal plant. Third, making a prototype identification system or the introduction of medicinal plants based on leaf morphological characteristics. The identification process is carried out using two approaches, namely the Manhattan Distance and Artificial Neural Networksimilar. In the testing phase of the resulting software prototype, 51 types of medicinal plant leaves were used where each type consisted of 10 different leaf images. Based on the trial results, the accuracy rate of identification or recognition of medicinal plants using Manhattan Distance is 99.0196%, and when using Neural Networks, the accuracy rate reaches 100% for training data and 84.31% for testing data.