| dc.contributor.advisor | RIYADI, SLAMET | |
| dc.contributor.advisor | DAMARJATI, CAHYA | |
| dc.contributor.author | HASIM, AHMAD WAKHID | |
| dc.date.accessioned | 2020-02-26T03:55:54Z | |
| dc.date.available | 2020-02-26T03:55:54Z | |
| dc.date.issued | 2019-08 | |
| dc.identifier.uri | http://repository.umy.ac.id/handle/123456789/32042 | |
| dc.description | Maintaining healthy teeth is important because teeth are risky to be affected by bad
bacteria. Because unhealthy teeth will result in cavities. Oral and dental health is a
problem that often occurs every year. Problems that often occur in the mouth are
dental caries or cavities.
U-net is a learning solution in assignments to quantification tasks that often occur
such as detecting membranes and structuring details in image settings. It cannot
enable the unlimited segmentation of large images that are altered by the overlaptile
strategy. U-net deep learning method achieves good results in the segmentation
of medical images. The u-net method is very important for overcoming problems
in segmenting specific dental images in the dentin section. This greatly helps the
dent inspection process quickly and minimizes errors due to manuals that can
provide advice between doctors, and can produce positive results.
The method of learning in the net achieves quite good results in the segmentation
of medical images. The steps per age that are set in the training are 2000, 2500, and
3000. The process consists of processes that produce dentine mask prediction
output. Determination of the results is divided into three categories, which are
commensurate, sufficient, and less. For comparable results that still need to be
improved for training data with parameter settings above 3000 | en_US |
| dc.description.abstract | Maintaining healthy teeth is important because teeth are risky to be affected by bad
bacteria. Because unhealthy teeth will result in cavities. Oral and dental health is a
problem that often occurs every year. Problems that often occur in the mouth are
dental caries or cavities.
U-net is a learning solution in assignments to quantification tasks that often occur
such as detecting membranes and structuring details in image settings. It cannot
enable the unlimited segmentation of large images that are altered by the overlaptile
strategy. U-net deep learning method achieves good results in the segmentation
of medical images. The u-net method is very important for overcoming problems
in segmenting specific dental images in the dentin section. This greatly helps the
dent inspection process quickly and minimizes errors due to manuals that can
provide advice between doctors, and can produce positive results.
The method of learning in the net achieves quite good results in the segmentation
of medical images. The steps per age that are set in the training are 2000, 2500, and
3000. The process consists of processes that produce dentine mask prediction
output. Determination of the results is divided into three categories, which are
commensurate, sufficient, and less. For comparable results that still need to be
improved for training data with parameter settings above 3000 | en_US |
| dc.publisher | FAKULTAS TEKNIK UNIVERSITAS MUHAMMADIYAH YOGYAKARTA | en_US |
| dc.subject | deep learning, deep learning u-net, image segmentation | en_US |
| dc.title | SEGMENTASI DENTIN MENGGUNAKAN METODE U-NET DEEP LEARNING | en_US |
| dc.type | Thesis
SKR
FT
419 | en_US |
