KUSUMA DEWI PURNOMO PUTRI, GABRIELLA ZAPTYNI DYAH (2022) CLASSIFICATION OF BANANA TYPES USING KNN (K-Nearest Neighbor) METHOD. ["eprint_fieldopt_thesis_type_tugasakhir" not defined] thesis, University of Technology Yogyakarta.
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Abstract
ABSTRACT Fruits are one of the many horticultural commodities that play an essential role in national economic development. One of them is bananas, in which almost all areas in Indonesia have banana plants. With so many types of bananas in Indonesia, it will cost quite a bit to select banana types on a large scale if only relying on human abilities. Many people do not understand the types of bananas. Based on these problems, this research will build a system that can classify the types of bananas. This study will use the GLCM (Gray Level Co-occurrence Matrix) method for the feature extraction process of image features and KNN (K-Nearest Neighbor) as a classification method. The system that will be built will have several features, such as image input, grayscale conversion, display feature extraction of GLCM features, perform classification using KNN, and display the results of KNN classification. The data used in this study were 200 data divided into 176 training data and 24 test data. The data were classified using the KNN method with values of K = 3, K = 5, K = 7, and K = 9. This study obtained the greatest accuracy, namely 95.8%, with a value of K = 7. Keywords: Banana, Classification, K-Nearest Neighbor (KNN), Gray Level Co
| Item Type: | Thesis (["eprint_fieldopt_thesis_type_tugasakhir" not defined]) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 30 Nov 2022 01:49 |
| Last Modified: | 30 Nov 2022 01:49 |
| URI: | http://eprints.uty.ac.id/id/eprint/11255 |
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