| dc.creator |
Ridwan, Ichsan |
|
| dc.date.accessioned |
2020-06-15T03:58:33Z |
|
| dc.date.available |
2020-06-15T03:58:33Z |
|
| dc.identifier |
http://eprints.ulm.ac.id/2494/1/_ajassp_2017_726_736_-_abstract7.pdf |
|
| dc.identifier |
Ridwan, Ichsan Identification of Characteristics of Land Cover in Mangkauk Catchment Area Using Support Vector Machine (SVM) And Artificial Neural Network (ANN). Identification of Characteristics of Land Cover in Mangkauk Catchment Area Using Support Vector Machine (SVM) And Artificial Neural Network (ANN), 14 (7). pp. 726-736. ISSN 1554-3641 |
|
| dc.identifier.uri |
https://repo-dosen.ulm.ac.id//handle/123456789/10730 |
|
| dc.description |
Abstract: Land cover is anything that includes any types of appearance on
the surface of the earth on a particular land. Information related to land
cover can be used as at the parameter to determine the amount of runoff in
a catchment area. This study was conducted in the Catchment Area
(CA) of Mangkauk using Landsat 8 OLI/TIRS 2014 scene path/row
117/62 with the methods of Support Vector Machine (SVM) and
Artificial Neural Network (ANN). The classification of land cover in
Mangkauk catchment area included forests, plantations, shrubs,
reeds/grasses, rice fields, open lands, settlements and water body. Based
on the accuracy test of land cover classification using SVM, the value
of the overall accuracy was 97.22% with Kappa Coefficient 0.96, while
using ANN 86.33% with Kappa Coefficient 0.79.
Keywords: ANN, Mangkauk Catchment Area, Land Cover, SVM |
|
| dc.format |
text |
|
| dc.publisher |
American Journal of Applied Sciences |
|
| dc.relation |
http://thescipub.com/abstract/10.3844/ajassp.2017.726.736 |
|
| dc.relation |
http://eprints.ulm.ac.id/2494/ |
|
| dc.subject |
Q Science (General) |
|
| dc.title |
Identification of Characteristics of Land Cover in Mangkauk
Catchment Area Using Support Vector Machine (SVM) And
Artificial Neural Network (ANN) |
|
| dc.type |
Article |
|
| dc.type |
PeerReviewed |
|