| dc.contributor.advisor | Herdiyeni, Yeni | |
| dc.contributor.author | Kulsum, Lies Umi | |
| dc.date.accessioned | 2023-11-07T07:38:19Z | |
| dc.date.available | 2023-11-07T07:38:19Z | |
| dc.date.issued | 2010 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/131020 | |
| dc.description.abstract | Identification house plant species automatically still be a problem for recognizing various kind of house plants. This research proposes a new method for identifying a house plant using Local Binary Patterns (LBP) descriptor and Probabilistic Neural Network (PNN). There are three kinds of LBP descriptor used in this research, i.e. , , and . Database of 300 house plant images belong to 30 different types of house plant in Indonesia are extracted using , , and based on texture feature and classified using PNN. The experimental result shows that has the best accuracy to indentify house plants with accuracy 73.33%. The proposed system is promising since it is capable to identify house plants species efficienty and accurately | id |
| dc.language.iso | id | id |
| dc.publisher | Bogor Agricultural University (IPB) | id |
| dc.subject.ddc | Mathematics and natural sciences | id |
| dc.subject.ddc | Computer science | id |
| dc.title | Identifikasi tanaman hias secara otomatis menggunakan Metode Local Binary Patterns descriptor dan Probabilistic Neural Network | id |
| dc.type | Undergraduate Thesis | id |
| dc.subject.keyword | Plant extraction | id |
| dc.subject.keyword | Local binary patterns | id |
| dc.subject.keyword | Texture feature | id |
| dc.subject.keyword | Probabilistic neural network | id |
| dc.subject.keyword | Bogor Agricultural University | id |
| dc.subject.keyword | Institut Pertanian Bogor | id |
| dc.subject.keyword | IPB | id |
