Judul: The Backpropagation Neural Networks Modelling Using Probabilistic Neural Network for Monsoon Onset Prediction Based on Global Climate Indices
Abstrak: Monsoon-onset is the key factor in agricultural production especially for crops. Monsoon-onset over Indonesia region is influenced by global climate phenomena such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole- Mode (IOD), Southern Oscillation Index (SOI) and El Nino Modoki (EMI). And thus, we used the global climate indices as predictors for neural networks input model. In this research we build a prediction model using Backpropagation Neural Networks (BPNN) method and Probabilistic Neural Networks (PNN) as classifier for monsoon-onset classification. This research has an objective to predict the monsoon-onset over Indramayu district with agronomic approach definition using Backpropagation Neural Networks and Probabilistic Neural Networks. Because of Indramayu district is one of the rice field center in Indonesia, the agronomic approaches that been used is especially for crops which has determined in Moron et al. (2009) research. This research divide into two scheme. First, monsoon-onset prediction using backpropagation neural networks model in five different architectures. This architectures consist of different hidden layer and hidden neuron combination. And the second is, monsoon-onset prediction using Backpropagation Neural Networks -based on first scheme results- with probabilistic Neural Networks as classifier. The Probabilistic Neural Networks are used for classify the monsoononset class in validation process for each new evaluated data. The first scheme shows that the backpropagation neural networks using 1 hidden layer and 10 hidden neuron as the best results with r 0.89 and RMSE 11.6. This results occurred at Kertasemaya location point. In the second scheme, Kertasemaya location point also gave us the best results in prediction with r , PNN accuration and RMSE as follows 0.85, 87.5% and 10.3. This results give us better number than previous scheme, with some notes. Taylor Diagram was used as a performance analysis tools for models evaluation. The last model (models which is developed in second scheme) become the best prediction model for 3 out 8 location points in the research. And also, we build a correlation map based on location points spreads in order to point out the spatial pattern. The final results shows that monsoon-onset over Indramayu district can be determined using agronomic approach. Monsoon-onset prediction with agronomic approaches has been built using Backpropagation Neural Networks models and Probabilistic Neural Network as classifer, also global climate indices as predictors.
Keyword: backpropagation neural networks, global climate indices, monsoononset, probabilistic neural networks, taylor diagrams.

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: 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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