Komparasi Metode Extreme Learning Machine (ELM) dan Multi-Support Vector Machine (Multi-SVM) pada Identifikasi Tanaman Herbal - Luluk Sarifah; Lailiyatus Sa’adah; Iis Setiana (Department of Mathematics, Hasanuddin University, 2025)
In Indonesia, there are more than 2.039 species of herbal medicinal plants, which sometimes have similarities and make it difficult to identify the type of herbal plant. The purpose of this study is to facilitate the identification of herbal plant species by comparing the performance of the Extreme Learning Machine (ELM) and Multi-Support Vector Machine (Multi-SVM) methods. The ELM method was created to overcome the weaknesses of feedforward artificial neural networks, especially in terms of learning speed, while the Multi-SVM method is an advanced development of the SVM method. The stages of this research begin with image input which is through previous data acquisition, data preprocessing, and then the identification with ELM and Multi-SVM methods. Based on the simulations that have been carried out, the average accuracy on training data for the ELM method is 93%, while the Multi-SVM method is 44%. Also, the average accuracy on testing data for the ELM method is 85%, while the Multi-SVM method is 40%.