Judul: Pemodelan Jaringan Syaraf Tiruan untuk Memprediksi Awal Musim Hujan Berdasarkan Suhu Muka Laut
Abstrak: Anjatan-Indramayu is the one of agriculture regions in Indonesia. The success or failure of the harvest each year depends on water availability in the region. Therefore we need an accurate method to predict the beginning of rainy season. The method used to predict in this research is artificial neural network (ANN) backpropagation. Result of ANN prediction accuracy is measured by R2 and RMSE. This research uses ECHAM4p5_CA sea surface temperature (SST) is one of the sea surface temperature models with the months of June, July and August. The domain of SST is selected based on the correlation of 5% and 10% for each of the month June, July, and August. This research uses ANN architecture with two parameters: neuron hidden (NH) and learning rate (LR). The number of hidden neurons used in this research are 5, 10, 20, and 40, and the learning rates are 0.3, 0.1, and 0.01. The best prediction result correlation of 5% using the ANN is for June with R2 is 51% and RMSE 3.03 at NH 10 and LR 0.01, July with R2 is 48% and RMSE 3.39 at NH 20 and LR 0.1, and August with R2 is 75% and RMSE 2.51 at NH 40 and LR 0.01. The best prediction result correlation of 10% using the ANN is for June with R2 is 44% and RMSE 3.32 at NH 5 and LR 0.3, July with R2 is 42% and RMSE 3.42 at NH 10 and LR 0.1, and August with R2 is 71% and RMSE 3.37 at NH 20 and LR 0.01. The conclusion from this research is the neuron hidden and learning rate with different values affect R2 and RMSE.
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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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