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: Downscaling Methodology Development Using Arftificial Neyral Network (Case Study of Indramayu’s Rainfall).
Abstrak: The unexpected climate change results a long period wet and dry so it makes many agricultural areas damage. This is cause the farmers failed to harvest so the production of food will be decreased. To solve this problem, we need a model that can estimate the climate and rainfall so it can determine the suitable cropping pattern with the climate. Statistical downscaling modeling commonly used to model the changes of the weather. This study develops a statistical downscaling model that use artificial neural network with some of GCM data as an input in training. In this study, the GCM data is input for training. Before training this data is grouped into 5 groups. The results obtained indicate that some of the GCM can approach the average of monthly rainfall. However, in some extreme point, the result have not managed to reach and close to results of observation. The best results obtained with the correlation Bondan station at 0.4976 and the worst results obtained Krangkeng station with a correlation of 0.3704. In this study also can be seen that the estimators can be influenced by the location of the observation. The location is adjacent to the sea tend to have the results of a relatively lower correlation compared with the location that far from 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