METODE KLASIFIKASI MUTU JAMBU BIJI MENGGUNAKAN KNN BERDASARKAN FITUR WARNA DAN TEKSTUR
| Main Authors: | Prahudaya, Taftyani Yusuf, Harjoko, Agus |
|---|---|
| Format: | Article info Classification application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Gadjah Mada
, 2017
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| Subjects: | |
| Online Access: |
https://jurnal.ugm.ac.id/teknosains/article/view/26972 https://jurnal.ugm.ac.id/teknosains/article/view/26972/17069 |
| ctrlnum |
article-26972 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">METODE KLASIFIKASI MUTU JAMBU BIJI MENGGUNAKAN KNN BERDASARKAN FITUR WARNA DAN TEKSTUR</title><creator>Prahudaya, Taftyani Yusuf</creator><creator>Harjoko, Agus</creator><subject lang="en-US">Computer Science, pattern recognition</subject><subject lang="en-US">Classification; Digital image processing; Guava; KNN</subject><description lang="en-US">Guava (Psidium guajava L.) is a fruit that has many health benefits. Guava also has commercial value in Indonesia and has a large market share. This indicates that the commodity of guava has been consumed by society extensively. This time the sorting process is still done manually which still has many shortcomings. This classification gives the classification results are less accurate and inconsistent due to the carelessness of humans. Grading process in the marketing sector is essential. Improper grading potentially detrimental to farmers because all the fruit quality were priced the same. Therefore, we need a consistent classification system.The system uses image processing to extract the color and texture features of guava. As a quality classification KNN method (K-Nearest Neighbor) is used. This system will classify guava into four quality classes, namely the super class, class A, class B, and external quality. KNN designed with input 7 features extraction which is the average value of RGB (Red, Green, and Blue), total defect area, and the GLCM value (entropy, homogeneity, and contrast) with the 4 outputs of quality. From the test results showed that the classification method is able to classify the quality of guava. The highest accuracy is obtained in testing K = 3 with 91.25% accuracy rate.</description><publisher lang="en-US">Universitas Gadjah Mada</publisher><contributor lang="en-US"/><date>2017-08-30</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Other:</type><type>Other:Classification</type><type>File:application/pdf</type><identifier>https://jurnal.ugm.ac.id/teknosains/article/view/26972</identifier><identifier>10.22146/teknosains.26972</identifier><source lang="en-US">Jurnal Teknosains; Vol 6, No 2 (2017): June; 113-123</source><source lang="id-ID">Jurnal Teknosains; Vol 6, No 2 (2017): June; 113-123</source><source>2443-1311</source><source>2089-6131</source><language>eng</language><relation>https://jurnal.ugm.ac.id/teknosains/article/view/26972/17069</relation><rights lang="en-US">Copyright (c) 2017 Taftyani Yusuf Prahudaya, Agus Harjoko</rights><rights lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</rights><recordID>article-26972</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other Other: Other:Classification File:application/pdf File Journal:eJournal |
| author |
Prahudaya, Taftyani Yusuf Harjoko, Agus |
| title |
METODE KLASIFIKASI MUTU JAMBU BIJI MENGGUNAKAN KNN BERDASARKAN FITUR WARNA DAN TEKSTUR |
| publisher |
Universitas Gadjah Mada |
| publishDate |
2017 |
| topic |
Computer Science pattern recognition Classification Digital image processing Guava KNN |
| url |
https://jurnal.ugm.ac.id/teknosains/article/view/26972 https://jurnal.ugm.ac.id/teknosains/article/view/26972/17069 |
| contents |
Guava (Psidium guajava L.) is a fruit that has many health benefits. Guava also has commercial value in Indonesia and has a large market share. This indicates that the commodity of guava has been consumed by society extensively. This time the sorting process is still done manually which still has many shortcomings. This classification gives the classification results are less accurate and inconsistent due to the carelessness of humans. Grading process in the marketing sector is essential. Improper grading potentially detrimental to farmers because all the fruit quality were priced the same. Therefore, we need a consistent classification system.The system uses image processing to extract the color and texture features of guava. As a quality classification KNN method (K-Nearest Neighbor) is used. This system will classify guava into four quality classes, namely the super class, class A, class B, and external quality. KNN designed with input 7 features extraction which is the average value of RGB (Red, Green, and Blue), total defect area, and the GLCM value (entropy, homogeneity, and contrast) with the 4 outputs of quality. From the test results showed that the classification method is able to classify the quality of guava. The highest accuracy is obtained in testing K = 3 with 91.25% accuracy rate. |
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DAERAH ISTIMEWA YOGYAKARTA |
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