KLASIFIKASI PERSENTASE IMPERVIOUS SURFACE AREA MENGGUNAKAN METODE BACKPROPAGATION - Ratna, Anni (Jurnal Ilmu Komputer dan Sistem Informasi, 2013)
Classification percentage of impervious surface area is a technique for classifying the percentage of impervious contained in an image. The purpose is to determine how much impervious surface in examined area. The image used is satellite image from OrbView-3. The method used in this design is Backpropagation Neural Network. NN has the ability similar to neural networks in the human brain. NN will learn the classifications such as the brain learn to recognize an object the more it will be learning more intelligent or introduction will be closer to the truth. The input used is the value of pixel R, G, B and NIR of the image, while its output is Vegetation, impervious, and Soil. The output of the program in the form of thematic maps and impervious percentage of the image under study. However, the percentange of error from the program is not known, because there is no accurate data on the percentage of impervious