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: 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: Katalis Zink Ferit Tersubstitusi Cu untuk Oksidasi Benzil Alkohol
Abstrak: Katalis berperan dalam menurunkan energi aktivasi. Katalis ZnFe2O4 dan ZnFe2O4 tersubstitusi Cu disintesis menggunakan metode kopresipitasi untuk digunakan dalam oksidasi benzil alkohol. Katalis dicirikan dengan spektrofotometer inframerah (FTIR) dan difraktometer sinar-X. Spektrum FTIR menunjukkan puncak serapan dari ikatan Zn-O dan Fe-O masing-masing pada rentang 559‒575 cm-1 dan 419‒447 cm-1. Difraktogram menunjukkan terbentuknya fase tunggal Zn0.6Cu0.4Fe2O4 pada katalis ZnFe2O4 tersubstitusi Cu 20%. Benzil alkohol dioksidasi dengan sistem refluks. Konversi benzil alkohol meningkat dengan meningkatnya suhu. Kadar benzaldehida yang dihasilkan dengan katalis ZnFe2O4 tersubstitusi Cu 20% lebih tinggi dibandingkan dengan katalis ZnFe2O4 yang tidak tersubstitusi dan yang tersubstitusi Cu 60 dan 80%. Hal tersebut menunjukkan bahwa katalis ZnFe2O4 tersubstitusi Cu 20% memiliki kinerja yang lebih baik dalam oksidasi benzil alkohol menjadi benzaldehida dengan kadar benzaldehida 8.763%.
Keyword: Benzil alkohol, katalis, oksidasi, ZnFe2O4