Identifikasi Tumbuhan Obat Herbal Berdasarkan Citra Daun Menggunakan Algoritma Gray Level Co-occurence Matrix dan K-Nearest Neighbor
Identification of Herbal Medicinal Plants Based on Leaf Image Using Gray Level Co-occurence Matrix and K-Nearest Neighbor Algorithms
Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
under http://creativecommons.org/licenses/by-sa/4.0.
Medicinal plants can be used as an alternative natural treatment, instead of chemical drugs. But because of too many types of plants and lack of knowledge, it will be difficult to identify these herbs. Computer assistance can be used to facilitate the identification of these herbs. This research proposes the identification of herbal plants based on leaf image using texture analysis. There are 10 types of herbal medicinal plants used in this study. The texture analysis used was GLCM by extracting contrast, correlation, energy, and homogeneity. Classification is done by KNN. The result of the experiment showed that the accuracy of identification using 9-fold cross-cross validation method was 83.33% using 9 subsets.
Note: This article has supplementary file(s).
| Subject | Dataset dan pengolahan data citra 10 daun herbal |
| Type | Data Set |
| Download (69KB) Indexing metadata |
Article Metrics:
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Last update: 2021-07-24 13:39:12
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Last update: 2021-07-24 13:39:13
-
Identification using the K -Means Clustering and Gray Level Co-occurance Matrix (GLCM) At Maturity Fruit Oil Head
Sukemi, Edi Sukrisno. 2019 Fourth International Conference on Informatics and Computing (ICIC), 2019. doi: 10.1109/ICIC47613.2019.8985681 -
Use of Support Vector Machine to Classify Rhizomes Based on Color
M Maimunah, E R Arumi. Journal of Physics: Conference Series, 127 , 2019. doi: 10.1088/1742-6596/1381/1/012031 -
Identification of Medicinal Plant Leaves Using Convolutional Neural Network
Yuanita A. Putri, Esmeralda C. Djamal, Ridwan Ilyas. Journal of Physics: Conference Series, 127 (1), 2021. doi: 10.1088/1742-6596/1845/1/012026
under http://creativecommons.org/licenses/by-sa/4.0.
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