Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)

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Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN)

Authors
Aeri Rachmad1, *, Rinci Kembang Hapsari2, Wahyudi Setiawan1, Tutuk Indriyani2, Eka Mala Sari Rochman1, Budi Dwi Satoto1
1Faculty of Engineering, University of Trunojoyo Madura, Bangkalan-Madura, Indonesia
2Faculty of Information Technology, Institute of Technology Adhi Tama, Surabaya, Indonesia
*Corresponding author. Email: aery_r@trunojoyo.ac.id
Corresponding Author
Aeri Rachmad
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-174-6_4How to use a DOI?
Keywords
Tobacco; GLCM; classification; K-NN
Abstract

Tobacco is one of the largest agricultural products and is widely traded in the world market, including in Indonesia. In Indonesia, tobacco leaves are used as raw material for cigarettes which are mostly produced by cigarette companies. The quality of tobacco leaves greatly affects the quality of cigarettes, this is because the condition of tobacco leaves is influenced by several factors including pests, diseases, and climate. This study uses the Gray Level Co-Occurrence Matrix (GLCM) method for texture feature extraction, while for classification uses the K-Nearest Neighbor (KNN) method to classify the quality of tobacco leaves. The data used in this study is the image of tobacco leaves taken directly in TonDowulan Village, Plandaan District, Jombang Regency at the age of the leaves of approximately 2 months. Tobacco leaf images used were 300 images consisting of 3 classes, namely Normal, Perforated, and Withered based on the level of leaf damage. The GLCM features used are Contrast, Correlation, Energy, Homogeneity, and Entropy which will then be classified using the KNN method where before performing feature extraction the data must be processed first at the preprocessing stage. The result of the training using GLCM and K-NN feature extraction produces the highest accuracy value when the neighbor value 1, pixel distance 3, and k-fold 2 are 83.33%.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
Series
Advances in Intelligent Systems Research
Publication Date
22 May 2023
ISBN
978-94-6463-174-6
ISSN
1951-6851
DOI
10.2991/978-94-6463-174-6_4How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Aeri Rachmad
AU  - Rinci Kembang Hapsari
AU  - Wahyudi Setiawan
AU  - Tutuk Indriyani
AU  - Eka Mala Sari Rochman
AU  - Budi Dwi Satoto
PY  - 2023
DA  - 2023/05/22
TI  - Classification of Tobacco Leaf Quality Using Feature Extraction of Gray Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN)
BT  - Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
PB  - Atlantis Press
SP  - 30
EP  - 38
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-174-6_4
DO  - 10.2991/978-94-6463-174-6_4
ID  - Rachmad2023
ER  -