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: Development of Probabilistic Neural Networks Models for Prediction of Rainfall Categories on Subseasonal to Seasonal Scales
Abstrak: Pengembangan model untuk memprediksi kategori curah hujan menjawab kebutuhan manusia dalam mengetahui kondisi cuaca dan iklim di masa yang akan datang. Model prediksi yang berfokus pada skala cuaca atau skala iklim dinilai tidak dapat sepenuhnya menjelaskan skala prediksi submusiman hingga musiman. Prediksi pada skala submusiman hingga musiman menjadi pengisi celah pada periode antara prediksi cuaca dan prediksi iklim. Pada penelitian ini, proses prediksi curah hujan pada periode submusiman hingga musiman dirancang untuk wilayah Kabupaten Indramayu. Perancangan model memanfaatkan beberapa parameter udara atas pada ketinggian 200 hPA dan 850 hPA yang diperoleh dari luaran General Circulation Model (GCM) S2S yang dikembangkan oleh ECMWF. Metode prediksi kategori yang dipilih untuk membangun model prediksi pada penelitian ini adalah Probabilistic Neural Networks (PNN) dengan memanfaatkan Principal Component Analysis (PCA) sebagai alat untuk melakukan reduksi dimensi pada prediktor. Data curah hujan yang digunakan untuk peubah terikat berupa data presipitasi yang dikeluarkan oleh CHIRPS. Model dievaluasi dengan menghitung akurasi model. Hasil percobaan membangun model dengan menggunakan n-component sama dengan 5 memiliki performa keseluruhan yang baik dengan rata-rata akurasi sebesar 82,177% dan waktu pelatihan model sebesar 76 menit., The development of models to predict rainfall categories answers human needs in knowing future weather and climate conditions. Prediction models that focus on the weather scale or climate scale are considered unable to fully explain the sub-seasonal to seasonal prediction scale. Predictions on a sub-seasonal to seasonal scale fill the gap in the period between weather prediction and climate prediction. In this study, the rainfall prediction process in the sub-seasonal to seasonal period was designed for the Indramayu Regency area. The model design utilizes several upper air parameters at 200 hPA and 850 hPA altitudes obtained from the General Circulation Model (GCM) S2S output developed by ECMWF. The category prediction method chosen to build the prediction model in this study is Probabilistic Neural Networks (PNN) by utilizing Principal Component Analysis (PCA) as a tool to reduce the dimensions of the predictors. The rainfall data used for the dependent variable is precipitation data issued by CHIRPS. The model was evaluated by calculating model accuracy. The experimental results of building the model using n-components equal to 5 had good overall performance with an average accuracy of 82.177% and a model training time of 76 minutes.
Keyword: Prediction, Rainfall, GCM, PNN, S2S

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%.
Keyword: