Judul: Statistics concept as neural network training stopping criteria to overcome limited data (study case in prediction of palm oil based- Surfactant-MESA surface tension)
Abstrak: There exist situations such as marine ecology where artificial neural network (ANN) implementation must cope with scarce data. Efforts to add more data in this sort of field will most likely be overlooked for being expensive and time consuming. Cross-validation comes across the opportunity of still using ANN to survive this poor data condition while not compromising its generalization. This paper proposed prediction output interval as stopping-criteria employed in 5-fold validation and demonstrated its performance when selecting best hidden layer number of neurons to be used for surfactant-MESA surface tension prediction based on only ten data pairs. Output interval building, derived from statistics concept, was gaining from the fact that there were two experiment repetitions from the case which could be generalized to cases with two measurement repetitions. Repetitions are expected to produce difference. A 95% confidence interval to estimate mean from these difference samples was then used to create output interval for ANN training. Backpropagation ANN with this added stopping criterion successfully gave 1.83 x 10-3 cross-validation MSE comparable to 2.72 x 10-3 of overfitted result, using only epoch and gradient of training MSE as stopping criteria. A slight variation on the width of the output interval, using sum of sample mean and standard deviation, also gave 3.04 x 10-4 MSE as best results. These intervals were found to perform well when set as constants, drawn from parametric statistical figures, around two times standard deviation of surface tension measurement differences from the experiment repetitions.
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Judul: Jaringan Syaraf Tiruan dan Algoritma Genetika dalam Pemodelan Kalibrasi (Studi Kasus: Tanaman Obat Temulawak)
Abstrak: The problems in prediction of calibration model are multicolinearity and the number of variables is larger than the number of observations. Principal Component Analysis-Artificial Neural Network-Genetic Algorithm (PCA-ANNGA) models were applied for the relationship between sample of concentration which is limited and transmittance data which is in large dimensions. A large number of variables were compressed into principal components (PC’s). From these PC’s, the ANN was employed for prediction of concentration. The principal components computed by PCA were applied as inputs to a backpropagation neural network with one hidden layer. The models was evaluated using GA for the best network structure on hidden layer. Root Mean Square Error (RMSE) for 80% training set and 20% testing set are 0.0314 and 0.5225, respectively. Distribution of data according to the percentage of training data and testing data were also very influential to obtain the best network structure with the smallest RMSE achievement. The best model for these methods is two layers Neural Network with eight neuron in the hidden layer.
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Judul: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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