Judul: Perbandingan Klasifikasi Back Propagation Neural Network dan Maximum likelihood dalam pemetaan sebaran lahan sawah dan tebu menggunakan data landsat ETM+ Multi Temporal,
Abstrak: Perolehan informasi tematik dari citra penginderaan jauh dapat dilakukan dengan proses klasifikasi, yang secara umum dibedakan dalam klasifikasi terawasi (supervised classification) dan tak terawasi (unsupervised classification). Klasifikasi terawasi dapat dibedakan menjadi 2, yaitu klasifikasi parametrik dan klasifikasi non parametrik. Perbedaan antara klasifikasi parametrik dan non parametrik adalah persyaratan statistik distribusi normal, dimana klasifikasi parametrik memerlukan informasi parameter statistik dari daerah sampel pelatihan yang terdistribusi normal, sedang klasifikasi non parametrik tidak mensyaratkan distribusi normal. Metode klasifikasi citra digital yang digunakan dalam penelitian ini adalah maximum likelihood classification/MLC (parametrik) dan metode back propagation neural network/BPNN (non parametrik).
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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: Durability of Specific Immunoglobulin (Ig) Y Againts Influenza and Covid-19 in Sunscreen
Abstrak: The benefit of specific immunoglobulin Y (Ig Y) as an active ingredient in personal care products such as sunscreen has not been properly studied. Our study aims to evaluate the reliability of specific IgY against influenza and the COVID-19 virus in a sample sunscreen product. Specific IgY influenza and COVID-19 viruses were inserted in the sample sunscreen at two concentrations of 0.1% and 0.25%. Three treatments were observed: control (C) sunscreen without IgY, treatment 1 (T1) sunscreen with 0.1% IgY, and treatment 2 (T2) sunscreen with 0.25% IgY. Organoleptic observations were carried out to monitor changes in aroma, color, and texture, and sunscreen sampling was done every week for 6 weeks. The durability of IgY was evaluated using an enzyme-linked immunosorbent assay (ELISA) and a neutralization assay. Organoleptic observation from weeks 1 to 6 showed consistency in its aroma, color, and texture. Enzyme-linked immunosorbent assay and neutralization assay (NA) results of the T1 and T2 groups showed negative results except for sample T1 at week 5, which showed a positive result in the Influenza A ELISA. Based on the results, it seems that IgY concentrations of 0.1% or 0.25% were not optimal. However, this negative result might be due to low concentration of the IgY and the presence of beeswax, which inhibits IgY and causes it to dissolve in distilled water, thus preventing it from being detected in the assay.
Keyword: Immunoglobulin Y, Sunscreen, Influenza, COVID-19, Biological Activity