ANANDA PRATAMA, IHSAN (2022) RECOGNITION OF BEAM NOTATION IMAGES USING K-NEAREST NEIGHBOR METHOD. Tugas Akhir thesis, University of Technology Yogyakarta.
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Abstract
Musical notes are often found on sheet music or sheet music and are used as an international standard for writing music. To read musical notes, one must first study them, while numeric notes and letter notes are easier to understand and read. A system has not yet been found that can assist ordinary people in reading musical notes from images taken from smartphone cameras. Therefore, in this study the authors built an image recognition system for musical notes to help lay people who want to learn to read musical notes on musical scores. This system was built using the K-Nearest Neighbor (KNN) method with feature extraction of Gray Level Co-Occurance Matrix (GLCM) from musical notes. The GLCM features used are dissimilarity, correlation, homogeneity, contrast, ASM, energy. The dataset used is an image dataset that has noise. The best accuracy result from the system built is 81.25% by using Euclidean distance calculation and k value of 7. Keywords: Not Beam, KNN, GLCM, Image Classification.
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 30 Nov 2022 01:54 |
| Last Modified: | 30 Nov 2022 01:54 |
| URI: | http://eprints.uty.ac.id/id/eprint/11257 |
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