Judul: Land Use Classification with Back Propagation Neural Network and The Maximum Likelihood Method: A Case Study in Ciliwung Watershed, West Java, Indonesia.
Abstrak: Ciliwung Watershed is located at West Java Province; this area ranges from 106°47'29” to 107°0'25" E and from 6°24'16" to 6°46'23"N, having hard relief with slope 15% – 30%. Ciliwung watershed area has 117 km in length and 347 km2, it is one of water catchments that suffering damage relatively serious from the upstream to the downstream. Nevertheless, in less than 10 years since 1985, the conservation area has changed into settlement area or cultivation area. The objectives of the research are to apply back propagation neural network and the maximum likelihood classification method for land use classification and compare the performance of the two methods. A one periods of Landsat-7 ETM+ (December 22, 2001) images with the path/row 122/065 was used for classification in the Ciliwung Watershed. This research compared parametric method (maximum likelihood) and non-parametric method (back propagation neural network) that occupied the same Landsat-7 ETM+ and the same training area. Six bands (band 1, 2, 3, 4, 5, 7) from Landsat Image were used as input data for both classification methods. In this case, band 6 and 8 on the image not utilized for this research because both of band have different resolution with the others. Before Landsat-7 ETM+ used for classification process, it was corrected geometrically, atmospherically, and topographically. The purpose of these corrections was to decrease the error that can occur during the making of training area and classification process afterward. Landuse was classified into 8 classes: tea garden, settlement, paddy field, grass, forest, farm, bush, and water body. The target of the training area was based on Ikonos image interpretation, instead of using field data. There were 4000 pixels used for whole class categories. There were several experiments was performed by using back propagation neural network method, which were by changing number of pixels (from 100 to 4000 pixels) and number of iteration (100-2000 iterations). Error matrix was used as accuracy measurement in order to compare both methods. Error matrix showed that both methods had difficulty in classifying water class and paddy field class due to the closeness of spectral value between those particular classes. This research is also showed that back propagation neural network giving a better accuracy level (81.5%) rather than maximum likelihood method (73.4%). Kappa statistic showed that classification result by using back propagation neural network method is nearly close to the real field condition due to the value is 0.759 (75%), which is close to 1 as an ideal case.
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Judul: Land Cover Classification of ALOS Data Using Back-propagation Neural Network Models
Abstrak: Land cover information is vital for supporting decision concerning the management of the environment and for understanding the causes and trends of human and natural processes on the earth surface. The extraction of useful information (i.e. land cover) from satellite images, by means of classification, is one of the most important technical problems of remote sensing. The aim of this research is to develop land cover classification method of ALOS data using Back-Propagation Neural Network classifier a case study in Bogor Botanical Garden – West Java. The targets of this research are: to investigate the effect of different input parameter to land cover classification result using Back-Propagation Neural Network and to assess accuracy of the classification result generated with different input and to compare accuracies to decide whether one model is superior to another.
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Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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