Judul: SVR optimization with PSO on statistical downscaling modeling for rainfall prediction in the dry season.
Abstrak: Kabupaten Indramayu merupakan salah satu kabupaten yang berada di Provinsi Jawa Barat dan dikenal sebagai daerah sentra produksi pangan khususnya padi di Indonesia. Kabupaten Indramayu rentan terhadap kekeringan maupun kebanjiran lahan pertanian yang dipengaruhi oleh fenomena global, seperti El-Nino and Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), dan Madden Julian Oscillation (MJO). Berbagai fenomena tersebut secara spesifikasi juga mempengaruhi keragaman hujan dan sifat hujan musiman di Kabupaten Indramayu. Oleh karena itu, memahami sistem keragaman iklim sangat penting untuk dilakukan dengan luaran GCM sebagai peubah prediktor dan luasan grid yang digunakan berupa grid 7×7 untuk prediksi curah hujan di musim kemarau. Model prediksi yang dikembangkan menggunakan Support Vector Regression (SVR) dan Particle Swarm Optimization (PSO) sebagai optimasi nilai parameter fungsi kernel SVR. Penelitian dibagi menjadi dua bagian. Bagian pertama menggunakan tiga fungsi kernel SVR yaitu kernel linear, kernel RBF, dan kernel polinomial tanpa optimasi parameter, sedangkan pada bagian ke dua optimasi nilai parameter dengan PSO digunakan pada fungsi kernel yang memiliki ukuran korelasi tertinggi dan RMSE terendah, dalam penelitian ini fungsi kernel terbaik adalah kernel polinomial memiliki ukuran korelasi 0.662 dan RMSE 25.035 setelah dioptimasi nilai parameter. Model luaran GCM terbaik adalah model CMC1-CanCM3 dengan ukuran korelasi 0.793 dan RMSE 21.685. Diagram Taylor digunakan untuk analisis performa keseluruhan model yang telah dibangun pada 15 stasiun hujan, berdasarkan pada Diagram Taylor stasiun hujan yang memiliki ukuran korelasi yang baik dan RMSE terendah diperoleh pada stasiun Bondan dengan rataan ukuran korelasi 0.796 dan RMSE 19.99, dan stasiun yang memiliki RMSE yang sangat besar yaitu 58.53 dan ukuran korelasi yang baik sebesar 0.743 diperoleh pada stasiun Bangkir., Indramayu is one of the districts in West Java Province and is known as the central areas of food production, especially of rice in Indonesia. Agricultural land of Indramayu district are vulnerable to drought and flooded affected by various global phenomenon, such as the El-Nino and Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and the Madden Julian Oscillation (MJO). Various specifications of that phenomenon also affects variability and the seasonal nature of rainfall in Indramayu district. Therefore, understanding the climate system variability is very important to do the GCM output as a predictor variables and the extent of grid is used in the of 7×7 grid for rainfall prediction in dry season. Predictive models was developed using Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) as the optimization parameter values of SVR kernel function. The study was divided into two parts. The first section uses three SVR kernel function that is a linear kernel, RBF kernel and polynomial kernel without parameter optimization, meanwhile in the second part the PSO optimization parameter values used for the kernel functions which have the highest correlation of size and lowest RMSE, in this study the best kernel function was has 0.662 polynomial kernel size correlation and 25.035 RMSE after optimized by parameter values. The GCM outputs best model is the CMC1-CanCM3 model with the correlation i.e 0.793 and 21.685 RMSE. The Taylor’s Diagram are used to analyzes whole model’s performance that has been built in 15 rain stations, based on the Taylor’s
Keyword: Statistical Downscaling, Support Vector Regression, Particle Swarm Optimization, Curah Hujan di Musim Kemarau, general circulation models, nilai parameter, rainfall in the dry season

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: Embriogenesis ikan buta (Astyanax fasciatus)
Abstrak: Ikan buta Astyanax fasciatus adalah spesies ikan unik dari famili Characidae yang menempati perairan dalam gua di Wilayah La Cueva Chica-San Luis Potasi-Mexico. Ikan ini memiliki mata yang tereduksi (Blind Cavefish) dan warna kulit merah muda, pucat serta mengkilap.
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