KLASIFIKASI MUTU BUAH JAMBU BIJI GETAS MERAH BERDASARKAN TEKSTUR MENGGUNAKAN GREY LEVEL CO-OCCURENCE MATRIX (GLCM) DENGAN KLASIFIKASI KNN
Guava is a fruit that contains lot of vitamin and is good for health. Guava also has a high level of demand in Indonesia and has a big market. This proves that guava is widely consumed by people and has a high level of competitiveness. During this classification quality of guava by making manual obs...
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2020-07-09.
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| 100 | 1 | 0 | |a I Gede Wirayudhana, . |e author |
| 245 | 0 | 0 | |a KLASIFIKASI MUTU BUAH JAMBU BIJI GETAS MERAH BERDASARKAN TEKSTUR MENGGUNAKAN GREY LEVEL CO-OCCURENCE MATRIX (GLCM) DENGAN KLASIFIKASI KNN |
| 260 | |c 2020-07-09. | ||
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| 500 | |a http://repository.upnvj.ac.id/7449/9/RIWAYAT%20HIDUP.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/7449/23/LAMPIRAN.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/7449/11/ARTIKEL%20KI.pdf | ||
| 520 | |a Guava is a fruit that contains lot of vitamin and is good for health. Guava also has a high level of demand in Indonesia and has a big market. This proves that guava is widely consumed by people and has a high level of competitiveness. During this classification quality of guava by making manual observations by looking directly at the physical surface of the fruit outside. This manual result classification in less effective and inconsistent. Digital image processing technology or Image Proseccing can be used to classify the quality of red brittle guava in accordance with the Indonesian National Standard, especially in terms of the texture outside of guava. This system uses image processing to extract texture characteristics outside the surface of guava. As a quality classification used the KNN (K-Nearest Neighbor) method. This system will classify guava into 3 quality classes, namely super class, class A, and class B ,. KNN is designed with input 4 features extraction of GLCM values (energy, homogeneity, correlation and contrast) using a 0 degree angle. From the test results it was found that this classification method is able to provide the best accuracy at k = 9 in the KNN method with an accuracy of 45.8% | ||
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