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: Rainy season length prediction using support vector regression
Abstrak: Letak Indonesia yang berada di antara Samudra Pasifik dan Samudra Hindia menyebabkan kondisi iklim di Indonesia dipengaruhi oleh kondisi iklim global di kedua samudra tersebut. Oleh karena itu, panjang musim hujan sulit diprediksi. Penelitian ini bertujuan untuk membuat permodelan prediksi panjang musim hujan (PMH) menggunakan metode support vector regression (SVR). Prediktor yang digunakan adalah southern oscillation index (SOI) dari Samudra Pasifik dan dipole mode index (DMI) dari Samudra Hindia. Model yang terbentuk kemudian dievaluasi menggunakan koefisien determinasi (R2) dan root mean square error (RMSE). Data yang digunakan pada penelitian ini adalah data PMH di 3 stasiun cuaca di Kabupaten Pacitan (Arjosari, Kebon Agung, dan Pringkuku) tahun 1982/1983 hingga 2011/2012 sebagai data observasi serta data SOI dan DMI tahun 1982 hingga 2011 sebagai data prediktor. Penelitian ini menghasilkan model prediksi pada tiap stasiun cuaca. Nilai R2 terbaik pada stasiun cuaca Arjosari, Kebon Agung, dan Pringkuku secara berturut-turut adalah 0.73, 0.63, dan 0.58. Sementara itu, nilai RMSE terbaik pada stasiun cuaca Arjosari, Kebon Agung, dan Pringkuku secara berturut-turut adalah 2.45, 3.23, dan 2.86., Climate condition in Indonesia is influenced by the global condition on both Pacific Ocean and Indian Ocean. This fact has caused difficulty in determining the length of rainy season in Indonesia. The goal of this research is to develop a model of rainy season length prediction using support vector regression (SVR). The predictors are southern oscillation index (SOI) from Pacific Ocean and dipole mode index (DMI) from Indian Ocean. The prediction model is then evaluated with determination coefficient (R2) and root mean square error (RMSE). The data used in this research is the length of rainy season data from 3 weather stations in Pacitan district (Arjosari, Kebon Agung, and Pringkuku) between 1982/1983 and 2011/2012 as the observation data and SOI and DMI between 1982 and 2011 as the predictor data. The result of this research is a prediction model for each climate station. The best R2 for Arjosari, Kebon Agung, and Pringkuku weather stations are 0.73, 0.63, and 0.58 respectively. Meanwhile, the best RMSE for Arjosari, Kebon Agung, and Pringkuku weather stations are 2.45, 3.23, and 2.86 respectively.
Keyword: DMI, panjang musim hujan, SOI, SVR

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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