Judul: Peramalan Data Deret Waktu Menggunakan Support Vector Regression berdasarkan Berbagai Tipe Kernel
Abstrak: Support vector regression (SVR) is a nonlinear method that uses the principle of risk minimization. SVR model uses the kernel function with regularization constant (C and ε). The kernel function which is commonly used in the SVR are linear, polynomial and radial basis function (RBF). Determination of kernel with its constants is required in SVR model. Grid search algorithm determines the best regularization constant combination based on the smallest error rate. SVR is able to replace the case of linear regression and forecasting. The accuracy of forecasting is very important. The type of time series data is divided into several models, i.e. constant, trend, polynomial, and seasonal model. The study simulated various model of time series data using SVR. Model that had been tested was evaluated by Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The simulation found that SVR model which used RBF kernel on the four types of time series data performed the best forecasting accuracy. In the application data, the RBF kernel with C of 10 and ε of 0.04 was implemented to build the SVR model. In the training data, the best SVR model produced MAE of 7.15436, RMSE of 10.03607 and MAPE of 0.86904%, while in the testing data the model produced MAE of 6.67387, RMSE of 9.41013 and MAPE of 0.865755%. Even if the testing data produced smaller error compared to training data, the difference of errors in the training and testing data was not statistically significant.
Keyword: Statistik, Regression, Kernel, stocks, SVR, time series

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: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
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