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: 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: 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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