KLASIFIKASI REMPAH DAUN BERDASARKAN CIRI TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN ALGORITMA K-NEAREST NEIGHBOR (KNN)
Spices are parts of plants that have a strong aroma. Spices are usually added to cooking to add aroma to make the dish more delicious or to disguise the fishy smell of fish or meat. Because spices are part of plants, they can be sourced from roots, stems, flowers, fruit, seeds, and leaves. Celery le...
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2023-06-22.
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| 100 | 1 | 0 | |a Weni Ariska, . |e author |
| 245 | 0 | 0 | |a KLASIFIKASI REMPAH DAUN BERDASARKAN CIRI TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN ALGORITMA K-NEAREST NEIGHBOR (KNN) |
| 260 | |c 2023-06-22. | ||
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| 500 | |a http://repository.upnvj.ac.id/24962/20/JURNAL.pdf | ||
| 520 | |a Spices are parts of plants that have a strong aroma. Spices are usually added to cooking to add aroma to make the dish more delicious or to disguise the fishy smell of fish or meat. Because spices are part of plants, they can be sourced from roots, stems, flowers, fruit, seeds, and leaves. Celery leaves and coriander leaves are types of spices that are often used in cooking, both have a similar shape but have different uses for each dish. Due to their similar shape, the two leaves are hard to tell apart just by looking at them directly. The process of distinguishing manually by looking directly at the leaves is prone to errors, especially if the objects being compared are in large numbers, it will take a lot of time so it is not effective enough in terms of time and effort. So a solution is needed to minimize the error rate in distinguishing the two spices. This study aims to create a classification model that can distinguish celery leaves from coriander leaves using a dataset in the form of 50 images of celery leaves and 50 images of coriander leaves taken with a camera phone and then extracting texture features using the Gray Level Co-occurrence Matrix (GLCM) method and algorithms K-Nearest Neighbor (KNN) as a classification algorithm. From the results of the research conducted, obtained an accuracy of 90%, a precision of 90% and a recall of 90%. | ||
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| 655 | 7 | |a Thesis |2 local | |
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