Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN)
Durian is one of the most popular fruits because it has a delicious taste and distinctive aroma. It has different shapes and types, especially from thorns and different colors and has fruit parts that are also not the same as other parts. In terms of fruit selection, care must be taken because cons...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2022-09-01
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| Series: | Journal of Applied Engineering and Technological Science |
| Subjects: | |
| Online Access: | https://journal.yrpipku.com/index.php/jaets/article/view/959 |
| _version_ | 1811211585608220672 |
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| author | Frencis Matheos Sarimole Achmad Syaeful |
| author_facet | Frencis Matheos Sarimole Achmad Syaeful |
| author_sort | Frencis Matheos Sarimole |
| collection | DOAJ |
| description |
Durian is one of the most popular fruits because it has a delicious taste and distinctive aroma. It has different shapes and types, especially from thorns and different colors and has fruit parts that are also not the same as other parts. In terms of fruit selection, care must be taken because consumers generally still find it difficult to distinguish physically identified types of Durian fruit due to limited knowledge of the types of Durian fruit and require a relatively long time and accuracy in sorting. Therefore, there is a need for a method to sort the types of Durian fruit effectively and efficiently. Namely image segmentation based on the classification of the types of Durian fruit to help consumers. The method used is Gray Level Co-Occurrence Matrices for feature extraction, while to determine the proximity between the test image and the training image using the K-Nearest Neighbor method based on texture based on the color of the Durian fruit obtained. Extraction features using the GLCM method based on angles of 0°, 45°, 90° and 135°. Then the KNN method is used for the classification of characteristic results using K = 3. In this study, 1281 data training was used and 321 data testing was used, resulting in an accuracy of 93%.
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| first_indexed | 2024-04-12T05:15:15Z |
| format | Article |
| id | doaj.art-d5109563ef1949aaab3953ed75d30c8f |
| institution | Directory Open Access Journal |
| issn | 2715-6087 2715-6079 |
| language | English |
| last_indexed | 2024-04-12T05:15:15Z |
| publishDate | 2022-09-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj.art-d5109563ef1949aaab3953ed75d30c8f2022-12-22T03:46:39ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792022-09-014110.37385/jaets.v4i1.959Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN)Frencis Matheos Sarimole0Achmad Syaeful1STIKOM Cipta Karya InformatikaMahasiswa Durian is one of the most popular fruits because it has a delicious taste and distinctive aroma. It has different shapes and types, especially from thorns and different colors and has fruit parts that are also not the same as other parts. In terms of fruit selection, care must be taken because consumers generally still find it difficult to distinguish physically identified types of Durian fruit due to limited knowledge of the types of Durian fruit and require a relatively long time and accuracy in sorting. Therefore, there is a need for a method to sort the types of Durian fruit effectively and efficiently. Namely image segmentation based on the classification of the types of Durian fruit to help consumers. The method used is Gray Level Co-Occurrence Matrices for feature extraction, while to determine the proximity between the test image and the training image using the K-Nearest Neighbor method based on texture based on the color of the Durian fruit obtained. Extraction features using the GLCM method based on angles of 0°, 45°, 90° and 135°. Then the KNN method is used for the classification of characteristic results using K = 3. In this study, 1281 data training was used and 321 data testing was used, resulting in an accuracy of 93%. https://journal.yrpipku.com/index.php/jaets/article/view/959ClassificationGray Level Co-occurrence MatrixK-Nearest NeighborsDurian fruit |
| spellingShingle | Frencis Matheos Sarimole Achmad Syaeful Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN) Journal of Applied Engineering and Technological Science Classification Gray Level Co-occurrence Matrix K-Nearest Neighbors Durian fruit |
| title | Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN) |
| title_full | Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN) |
| title_fullStr | Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN) |
| title_full_unstemmed | Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN) |
| title_short | Classification of Durian Types Using Features Extraction Gray Level Co-Occurrence Matrix (GLCM) AND K-Nearest Neighbors (KNN) |
| title_sort | classification of durian types using features extraction gray level co occurrence matrix glcm and k nearest neighbors knn |
| topic | Classification Gray Level Co-occurrence Matrix K-Nearest Neighbors Durian fruit |
| url | https://journal.yrpipku.com/index.php/jaets/article/view/959 |
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