Judul: The Modeling of Downscaling GCM output and SST Anomaly Nino 3.4 Using Support Vector Regression (A Case Study of The monthly Rainfall In Indramayu).
Abstrak: The objective of this research is to develop a downscaling model GCM output and SST anomaly Nino 3.4 as input in the training to predict a rainfall monthly in Indramayu. The techniques of a downscaling is used for a phenomenon indicators of El Nino and Southern Oscillation (ENSO) climate anomaly such as a Global Circulation Model (GCM) and Sea Surface Temperature (SST) nino 3.4 are commonly used as a primary study learn and understand the climate system. This research propose a method for developing a downscaling model GCM output and SST anomaly Nino 3.4 by using Support Vector Regression (SVR). The research showed that GCM output and SST anomaly Nino 3.4 can be approach the average value of monthly rainfall. The best result of prediction is Bondan station which has average correlation that is 0.700.
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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: Bakteri Flora Paling Umum Ditemukan Di Rongga Mulut Python Retikulatus Liar
Abstrak: Reticulated python is a non-venomous snake native to South and Southeast Asia. It is the world's longest snake. The purpose of this literature review study is to investigate the common bacterial flora found in oral cavity of the reticulated python. The data for this case study was obtained from several articles, journals, books and credible websites. Based on existing data, there are a wide variety of bacteria in the oral cavity of free-living reticulated pythons. The majority of the organisms were Gram-negative. Pseudomonas spp. were the most commonly isolated bacterial species, being found in 50% of the snakes sampled, and was followed by Staphylococcus sciuri.
Keyword: Reticulated python, snake bites, bacterial flora, oral cavity, microbiology