Judul: Pengembangan model pemrograman paralel pada kalibrasi data untuk rekonstruksi data curah hujan
Abstrak: In the field of climatology with the long-term period, the Regional Climate Mode version 3 (RegCM3) is a model of spatially oriented and able to produce regionalscale information that can be used to forecast rainfall. However, the data RegCM3 is a model of mathematics and physics and not based on real observations. Therefore, it is necessary to calibrate the model RegCM3 based observation points to reconstruct the rainfall data. Because of the large number of RegCM3 and observation points, the process requires a large number of resources for computing and computational time also. Therefore we need a technique to reduce computational time. One way to solve this problem is to use parallel programming. The purpose of this study is to develop a system to reconstruct the rainfall (rainfall forecasting) using data RegCM3 based observation points using parallel programming approach to accelerate the process of computing. The scope of research is the rainfall data from Java and rainfall observation data Indramayu to observe the performance of accuracy and computation time are done in parallel. The highest accuracy performed by the system is 89.14%. The overall system using parallel programming is faster than serial program for large-sized data. This is showed by the value of speedup which is above 1.
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Judul: Statistical Downscaling of GCM Data using Support Vector Regression to Predict Monthly Rainfall in Indramayu.
Abstrak: The knowledge about weather and climate pattern, especially rainfall is required by many sectors such as, agricultural, plantation, transportation, and so on. Recently, the model that is used to observe the impact and to predict the climate change is the global circulation model (GCM). However, the current GCM-resolution data is too low and it is difficult to predict the local climate pattern that requires high resolution. However, it is possible to obtain regionalscale information if it is combined with statistical downscaling (SD) methods. The main objective of this research is to develop SD models by using support vector regression (SVR) in forecasting monthly rainfall in Indramayu, in order to get the accurate climate information based on GCM data. The results of this research showed that overall, the model is good enough to predict rainfall with normal conditions. But for extreme circumstances, although the prediction model was able to follow the pattern of the observational data, but the value of the resulting predictions have not managed to predict accuratelly the actual observed values. Variations of the evaluation of estimated results by the model for 13 rain stations in Indramayu district showed that the location of rain stations in certain areas can influence and determine the accuracy of the monthly rainfall forecast. Rain observations points located farther from the sea tend to have better estimation results than the observation points located close to the sea.
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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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