Rhizome Image Classification Using Support Vector Machine
- 10.2991/assehr.k.220301.164How to use a DOI?
- svm; rimpang; clasification
Rhizomes, also called rootstalks, are stems that help plants to reproduce asexually, survive in winter, store food, and make stem tubers. They possess many functions and merits. Some of the commonly found rhizomes are ginger, turmeric, and galangal, yet still a lot of people still find it difficult to distinguish those three rhizomes. That’s because those mentioned rhizomes do share several similarities in their shape and texture. This research submits a rhizome identification system with SVM (Support Vector Machine) classification method. Based on the experiments done, this particular method is chosen because it showed great results, quite high-valued accuracy level for data classification, and has minimum error rate. The types of rhizomes used in this research’s dataset are those three varieties mentiones above, while the amount of images in this experiment consists of 150 training images and 30 testing images. The experiment is done by calculating the accuracy value from data testing classification of three classes, which are ginger class, turmeric class, and galangal class utilizing the mentioned method. This rhizome identification system that uses the SVM classificafion method gets 78% accuracy value.
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Saniyatul Mawaddah AU - Mohammad Robihul Mufid AU - Arif Basofi AU - Agung Fiyanto AU - Darmawan Aditama AU - Nadiya Nurlaila PY - 2022 DA - 2022/03/04 TI - Rhizome Image Classification Using Support Vector Machine BT - Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021) PB - Atlantis Press SP - 990 EP - 993 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.220301.164 DO - 10.2991/assehr.k.220301.164 ID - Mawaddah2022 ER -