Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN
The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality,...
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| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2019-10-31.
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| LEADER | 02582 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_47906 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Listya, Friska Ayu |e author |
| 100 | 1 | 0 | |a department computer science |e contributor |
| 700 | 1 | 0 | |a Rokhman, Nur |e author |
| 245 | 0 | 0 | |a Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2019-10-31. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/47906 | ||
| 520 | |a The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality, the visual observation process is strongly influenced by subjectivity so that in certain conditions such as tired eyes and the number of oranges that want to classify too many the process can be inconsistent and also take a long time. Therefore, a technology is needed to accelerate the classification process and make it more objective. This study combines the Gray level Co-occurrence Matrix (GLCM) method for texture, Hue, Saturation, Value (HSV) features for color features and the k-Nearest Neighbor (k-NN) classification method. The data used were 60 images of rotten tangerines and 60 images of not rotten tangerines divided using a 4-fold cross-validation method to find the best combination of data training and data testing. 3 main processes will be carried out, namely preprocessing, feature extraction and classification. This study produced the highest accuracy of 80% from the combined of GLCM and HSV features extraction with value k = 5 for k-NN . | ||
| 540 | |a Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Classification; GLCM; HSV; K-NN | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 4 (2019): October; 357-368 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/47906/26042 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/47906 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/47906/26042 |z Get Fulltext |