Judul: Spatial extreme rainfall forecast (case study: monthly rainfall in Indramayu Regency)
Abstrak: Global climate change could increase the extreme events such as extreme rainfall, extreme temperatures, and windstorm intensity. The study of extreme rainfall prediction is necessary to be conducted to minimize the impact of the global climate change. Thus, statistical method is needed to explain the extreme rainfall events. Extreme rainfall events are usually measured for multiple locations, so it is necessary to use spatial extreme modeling to predict the extreme rainfall which takes into account the spatial correlation in the modeling. In spatial, nearest neighbors have a fairly close relationship, therefore the spatial correlation is used to determine how closely the relationship between locations. In this study, spatial correlation was calculated by using madogram and extremal coefficient. Madogram is a modification of semivariogram and extreme value distributions. Extremal coefficient is a metric characterization of tail dependence. Extreme spatial modeling were analyzed with copula approach and max-stable process. Copula approach assumes the marginal distribution of extreme values follow the uniform distribution. Max-stable process transform the marginal distribution of the extreme values to the Fréchet distribution. In this study, copula approach is used to spatial extreme modelling. The data used were monthly rainfall in the period of 1979-2008 at 15 weather stations in Indramayu Regency. To see the stability of the parameters, the data were divided into three groups: the data for 1979-2003, 1982-2006, and 1984-2008. Modeling for prediction of extreme rainfall using rainfall data of 1979-2007 and the data in 2008 are used for model validation. For the estimation of extreme rainfall rainy season, the data used is the data for 1979-2006, while the rainfall rainy season data of 2007 and 2008 are used for model validation. Madogram and extremal coefficient showed spatial correlation in Indramayu Regency. Copula parameter estimation for 1979-2003, 1982-2006, 1984-2008 year interval showed that the parameter estimate of extreme rainfall in Indramayu Regency tends to be stable. The results of this study showed that the best prediction value for annual period was for the next 12 months and this prediction is quite relevant to be used in the field. In the rainy season, the best prediction was for the next 6 months and this prediction is quite relevant to be used in the field.
Keyword: copula, madogram, extremal coefficient, spatial extreme rainfall

Judul: Regresi Kuantil dengan Kuadrat Terkecil Parsial dalam Statistical Downscaling untuk Pendugaan Curah Hujan Ekstrim
Abstrak: Rainfall as a part of the highest climate fluctuation and characterize the most dominant climate in Indonesia is strongly influenced by global climate change, such as extreme rainfall. Extreme rainfall can cause flood and various disadvantages like crop failure in agriculture. The analysis that examines the extreme events is needed to minimize the bad impact due to the extreme rainfall events. Extreme rainfall can be analyzed such as using statistical downscaling (SD). SD is the process of transforming information from large-scale (global) as an explanatory variables to small-scale (local) as a respon variable. In this research, monthly raifall data from Indramayu district used as respon variable and GCM output data used as explanatory variables. The precipitation of GCM output is high dimension and there are multicolinear between adjacent grids. To reduce the high dimension and solve the multicolinearity problem usually uses principal component analysis (PCA), functional principal component (FPCA) and partial least squares (PLS). PCA and FPCA focus on variety in the explanatory variables, whereas PLS focuses on the variety between the explanatory variables and the response variable. SD model requires a strong correlation between the precipitation of GCM output and the rainfall to obtain a more accurate estimate. Strong correlation delivers the same pattern of the two variables. A method to estimate the extreme rainfall in SD modeling is quantile regression, which is an expansion from median regression on various quantile value. The model that has been formed in quantile regression can be used to measure the effect of explanatory variables at the centre, right or left tail of the data distribution. The aim of this research is to estimate the extreme rainfall using quantile regression and PLS as a dimension reduction method with time lag precipitation of GCM output. The monthly rainfall of Indramayu and the precipitation of GCM output from 1979 to 2008. Data from 1979 to 2007 for developing the model and data 2008 for model validation. Dimension reduction using PLS produces one component i.e the first score of and the first score of . Then, the extreme was estimated by linier, quadratic and cubic quantile regresssion with score as explanatory variable and rainfall data as respon variable at the 75th, 90th and 95th quantiles. Model validation and consistency were implemented at the last step of this research. The results show that the rainfall estimate using linier, quadratic and cubic quantile regression model were not following the pattern of actual rainfall. The model was modified by adding dummy variables. Using dummy variables resulted better estimate. The best model is cubic quantile regression with dummy variables. The pattern of extreme rainfall was similar to the pattern actual rainfall. In February, the actual rainfall is about 439.33 mm and well predicted by the 95 th quantile. The cubic quantile regression with dummy variables can also result the consistent prediction for next two years .
Keyword: global circulation model, partial least square regression, quantile regression, statistical downscaling

Judul: Aktivitas Keratinase Bacillus licheniformis dalam Memecah Keratin Bulu Ayam
Abstrak: Tepung bulu ayam sangat potensial dikembangkan menjadi bahan baku pakan sebagai sumber protein, karena kandungan proteinnya tinggi. Namun kendala yang dihadapi adalah kandungan senyawa kreatin yang cukup tinggi. Kreatin merupakan peotein serat, tidak larut dalam air dan sulit dicerna. Rantai peptida dalam keratin terbentuk dalam bentuk pilin atau beliks dan saling berhubungan dengan ikatan disulfida dan ikatan hidrogen. Hal inilah yang menyebabkan kreatin sulit dicerna.
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