| dc.contributor.author | Kelana Jaya, Indra | |
| dc.contributor.author | Buono, Agus | |
| dc.contributor.author | Arkeman, Yandra | |
| dc.date.accessioned | 2014-01-24T03:09:43Z | |
| dc.date.available | 2014-01-24T03:09:43Z | |
| dc.date.issued | 2013-09 | |
| dc.identifier.issn | 2354-9041 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/67364 | |
| dc.description.abstract | Classifications of precipitation on dry season are divided into three classes. The classes are low precipitation, medium and high intensity precipitation. The precipitation average uses as input for divided classes. The data divided by normal distribution form. The Global climate indices use as predictor’s for classification. The correlation method used to select predictor’s. The result of predictor’s selection became an input vector in classification. The learning vector quantization is use to predict the classes using input vector. The accuracy from this method is 71.05% with 100 epoch and learning rate 0.002, 0.004 and 0.005. The next research we suggest how to choose an optimal learning rate | en |
| dc.language.iso | en | |
| dc.publisher | Departement of Agroindustrial Technology, George Mason University, Indonesian Agroindustri Association | |
| dc.title | Precipitation Classification Using LVQ on Dry Season Based on Global Climate Indices Case Study in Indramayu District | en |
| dc.type | Article | en |
| dc.subject.keyword | Indian Ocean dipole | en |
| dc.subject.keyword | learning rate | en |
| dc.subject.keyword | learning vector quantization | en |
| dc.subject.keyword | sea surface temperature | en |
| dc.subject.keyword | southern oscillation index | en |
