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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science Indonesian Research Journal on Education
Shalahudin, Mohammad Imam
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Religion Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine Chusna, Nuke L.; Shalahudin, Mohammad Imam; Riyanto, Umbar; Alexander, Allan Desi
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.267 KB) | DOI: 10.47065/bits.v4i1.1624

Abstract

Mushrooms are plants that have high nutritional content and have various benefits for the health of the human body. However, not everyone knows the types of mushrooms that are suitable for consumption. The types of mushrooms have their own characteristics when viewed from the image. For this reason, a system is needed by utilizing digital image processing to classify types of mushrooms suitable for consumption, so that people can find out which types of mushrooms are suitable for consumption. This research is to classify types of mushrooms suitable for consumption using the Multiclass SVM algorithm with first-order feature extraction, which performs feature extraction based on the characteristics of the image histogram. The result of feature extraction is used as input for classification in Multiclass SVM. Multiclass SVM can map data points to dimensionless space to obtain hyperplane linear separation between each class. The developed method is implemented in Matlab, in order to produce a system in the form of a GUI so that it can be used by general users easily. Based on the test results, the average accuracy is 83%.
Co-Authors Alexander, Allan D Arisantoso Arisantoso Chusna, Nuke L. Daniarti, Yeni Moh. Erkamim Mulyadi Mulyadi Rahmadian, Jefri Riyanto, Umbar Soares, Teotino Gomes Sulistianto, Hafizh Sogar