Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds
Wild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicina...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4755 |
| _version_ | 1797327123080282112 |
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| author | Hendra Mayatopani Nurdiana Handayani Ri Sabti Septarini Rini Nuraini Nofitri Heriyani |
| author_facet | Hendra Mayatopani Nurdiana Handayani Ri Sabti Septarini Rini Nuraini Nofitri Heriyani |
| author_sort | Hendra Mayatopani |
| collection | DOAJ |
| description | Wild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicinal properties, especially the leaves. The purpose of this research is to classify the image of weed leaves with medicinal properties based on color and texture characteristics with an artificial neural network using a Self-Organizing Map (SOM). To improve information in feature extraction, RGB and HSV color features are used as well as texture features with Gray Level Co-occurrence Matrix (GLCM). Furthermore, the results of feature extraction will be identified as groups or classes with the Self-Organizing Map (SOM) algorithm which divides the input pattern into several groups so that the network output is in the form of a group that is most similar to the input provided. The test produces a precision value of 91.11%, a recall value of 88.17% and an accuracy value of 89.44%. The results of the accuracy of the SOM model for image classification on medicinal weed leaves are in the good category. |
| first_indexed | 2024-03-08T06:34:53Z |
| format | Article |
| id | doaj.art-00c76d9cd7d94b38be40ddf9a0399234 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T06:34:53Z |
| publishDate | 2023-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-00c76d9cd7d94b38be40ddf9a03992342024-02-03T10:32:52ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-06-017343744410.29207/resti.v7i3.47554755Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal WeedsHendra Mayatopani0Nurdiana Handayani1Ri Sabti Septarini2Rini Nuraini3Nofitri Heriyani4Universitas PraditaUniversitas Muhammadiyah TangerangUniversitas Muhammadiyah TangerangUniversitas NasionalUniversitas Muhammadiyah TangerangWild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicinal properties, especially the leaves. The purpose of this research is to classify the image of weed leaves with medicinal properties based on color and texture characteristics with an artificial neural network using a Self-Organizing Map (SOM). To improve information in feature extraction, RGB and HSV color features are used as well as texture features with Gray Level Co-occurrence Matrix (GLCM). Furthermore, the results of feature extraction will be identified as groups or classes with the Self-Organizing Map (SOM) algorithm which divides the input pattern into several groups so that the network output is in the form of a group that is most similar to the input provided. The test produces a precision value of 91.11%, a recall value of 88.17% and an accuracy value of 89.44%. The results of the accuracy of the SOM model for image classification on medicinal weed leaves are in the good category.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4755image classificationartificial neural networksself-organizing mapsmedicinal weed leaves |
| spellingShingle | Hendra Mayatopani Nurdiana Handayani Ri Sabti Septarini Rini Nuraini Nofitri Heriyani Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) image classification artificial neural networks self-organizing maps medicinal weed leaves |
| title | Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds |
| title_full | Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds |
| title_fullStr | Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds |
| title_full_unstemmed | Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds |
| title_short | Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds |
| title_sort | implementation of self organizing map som algorithm for image classification of medicinal weeds |
| topic | image classification artificial neural networks self-organizing maps medicinal weed leaves |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4755 |
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