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: Pemodelan Statistical Downscaling dengan Regresi Kuantil untuk Pendugaan Curah Hujan Ekstrim (Studi Kasus Stasiun Bangkir Kabupaten Indramayu)
Abstrak: Statistical downscaling (SD) is a technique used to model the relationship between global-scale data and local-scale data with statistics model. The global-scale outcomes of Global Circulation Model (GCM) are used as independent variables in SD. Various methods of SD include multiple regression analysis, principal component regression analysis and artificial neural networks. However, these methods can not accurately predict extreme events. Quantile regression can be used to detect extreme conditions, both extreme dry and extreme wet. The aim of this study was to predict the extreme event and its probabability. The data of independent variables used were monthly rainfall of the district Indramayu Bangkir station. Quantile regression method was used to predict extreme rainfall and logistic regression to estimate the chances of extreme events. Quantile regression models formed had correct prediction rate in the 90th quantile in February. The probability of extreme rainfall events in quantile 75 was high in November, December, January, February and March. However, the occurrence probability in quantile 90 and 95, was only high in February. The prediction and probability of extreme rainfall based on quantile regression models and logistic regression showed similar trend with the data pattern observed in extreme conditions.
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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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