Judul: Peramalan Awal Musim Hujan Menggunakan Jaringan Syaraf Tiruan Backpropagation Levenberg-Marquardt
Abstrak: El Nino and La Nina cause changes to period of the rainy season in Indonesia. The condition causes negative effect directly on agriculture and indirectly on economy. In agriculture, these symptoms lead to crop failure resulting in increased food prices so that people's purchasing power decreases. One solution to reduce the negative impact of El Nino and La Nina is by predicting the monsoon season every year so that the government can determine the right types of plants according to seasonal conditions in a given year. This study aims to predict the beginning of the rainy season using feedforward neural networks. In general, feedforward neural network is used in the forecasting process. SOI data is used as a predictor and backpropagation is used as a neural network learning algorithm. We tested four groups of neural network architectures called Group 5, Group 10, Group 15, and Group 20 based on the number of neurons in the hidden layer. This study uses a leave-one-out cross-validation to validate forecasting models and simulation to see the results of the forecasting model. At the leave-one-out cross-validation, the best result is achived by Group 20 with an RMSE of 0.22 dasarian and a correlation coefficient of 0.99. In the simulation of forecasting the best result is obtained by Group 10 with an RMSE of 1.4 dasarian and a correlation coefficient of 0.8. We conclude that the best neural network architecture for predicting the beginning of the rainy season is Group 10. In addition, SOI month, suitable for predicting the beginning of the rainy season is June, July, and August.
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Judul: Optimasi Jaringan Saraf Tiruan Menggunakan Algoritme Genetika untuk Peramalan Panjang Musim Hujan
Abstrak: The focus of this research is to predict length of the rainy season in Indramayu using artificial neural networks (ANN) optimized by genetic algorithm (GA). The weights generated by the neural network is random, so the result of predictive value can change in each training. GA is a computational model that has operator selection, crossover, and mutation for generating a new population. The initialization of weights that is given by ANN will be optimized using GA. The predictors which are used in this research are the southern oscillation index (SOI) data and the beginning of rainy season (AMH) data with the length of rainy season data in previous year to predict the length of rainy season in the current year. The best result is obtained from the model on average region. On this region, the RMSE amounts to 14 days and the correlation coefficient between the observed values and predicted values amounts to 0.741 at 10% significance level, the RMSE amounts to 19 days and the correlation coefficient between the observed values and predicted values amounts to 0.694 at 5% significance level.
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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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