Judul: Optimasi Support Vector Regression Menggunakan Genetic Algorithm dan Particle Swarm Optimization Untuk Prediksi Curah Hujan Musim Kemarau
Abstrak: Support Vector Regression (SVR) is Support Vector Machine (SVM) that is used for regression case. SVM is a set of classification and regression technique which is development of non-linear algorithms. Regression method has been commonly used for prediction models e.g. for seasonal climate prediction. SVR process requires kernel functions to transform the non-linear inputs into a high dimensional feature space. This research focused on predictive modeling rainfall in the dry season at 15 weather stations in Indramayu district. The basic method used in this study was SVR optimized by a hybrid algorithm GAPSO (Genetic Algorithm and Particle Swarm Optimization). This researchbegan by identifying and formulating problem. It then continued by data preprocessing i.e. collecting the dataand determining training data and test data. Data processing were performed by process data with SVR optimized by using GAPSO, testing, and the last steps wereanalyzing and evaluating the results predicted and observed values. In the collecting phase of the data,Indian Ocean Dipole (IOD) index known as the Dipole Mode Index (DMI) and NINO 3.4 Sea Surface Temperature Anomaly (SSTA) index data were used as predictors (independent variables) while dry season rainfall was used as response variables (dependent variables). The data used in this study were from 1978 to 2008. In SVR process, we used data of 20 years as datatrainingand data of 10 years as data testing. SVR kernel function used in this study was RBF kernel.SVR has cost and kernelfunctionparameter valuethat should be optimized i.e. C and γ, respectively. These values givebig impact on the SVR model. The more the optimal value of C and γ,the better the model. SVR was optimized by using the hybrid technique GAPSO to obtain optimal value. Thistechniqueincorporates concepts from GA and PSO and it creates individuals of new generation not only by crossover and mutation operation in GA, but also through the process of PSO. SVR model was obtained by using 24 populations, 100 times iteration for each GA and PSO, 10 iterations of GAPSO. It was designed by using C andγparameters, ranging from 0.1 to 50 and 0 to 10, respectively. This research obtained a model of SVR with the highest correlation coefficient of 0.87 and NRMSE error value of 10.43 at Tugu station. Cikedung station has the lowest NMRSE error value of 9.01 and the correlation coefficient of 0.78.
Keyword: rainfall in dry season, Genetic Algorithm, Particle Swarm Optimization, Support Vector Regression

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: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
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