Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021)

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Rhizome Image Classification Using Support Vector Machine

Authors
Saniyatul Mawaddah*, saniyatul@pens.ac.id, Mohammad Robihul Mufidmufid@pens.ac.id, Arif Basofiariv@pens.ac.id, Agung Fiyantofiyan@it.student.pens.ac.id
Department of Informatic and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
Darmawan Aditamaaditama@pens.ac.id, Nadiya Nurlailanadiyaisr17@mb.student.pens.ac.id
Department of Creative Multimedia Technology, Politeknik Elektronika Negeri Surabaya, Indonesia
Corresponding Author
Saniyatul Mawaddahsaniyatul@pens.ac.id
Available Online 4 March 2022.
DOI
10.2991/assehr.k.220301.164How to use a DOI?
Keywords
svm; rimpang; clasification
Abstract

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.

Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

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<Previous Article In Volume
Volume Title
Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
4 March 2022
ISBN
978-94-6239-547-3
ISSN
2352-5398
DOI
10.2991/assehr.k.220301.164How to use a DOI?
Copyright
© 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

risenwbib
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  -