Open AccessUse of Support Vector Machine to Classify Rhizomes Based on Color
Author(s) -
M Maimunah,
Endah Ratna Arumi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1381/1/012031
Subject(s) - rhizome , artificial intelligence , support vector machine , feature extraction , computer science , rgb color model , pattern recognition (psychology) , digital image , identification (biology) , process (computing) , computer vision , image (mathematics) , image processing , botany , biology , operating system
Rhizome plants are one type of herbal medicinal plants which have various types and benefits. To find out the types of rhizome plants can be done by the classification process. Classification of rhizome plants can be done by identifying the color of the flesh of the rhizome. Identification technology in this study is needed to accelerate and facilitate the process of identifying rhizome species in the form of digital image data. Digital image processing can be used to identify rhizomes based on color using the support vector machine (SVM). In this study, the type of rhizome used as an input image was temulawak, temu hitam and temu mangga. Image preprocessing is done to get the color image of the rhizome flesh which is then used in the feature extraction stage. Image feature extraction process is done to get the color characteristics of the image by calculating the image RGB value. The image color value obtained has been used to identify rhizome species using SVM. The results obtained indicate that the identification of rhizome types using SVM provides an accuracy of 87.5%.