One of the main reasons for using asynchronous design is that it offers the opportunity to exploit the datadependent latency of many operations in order to achieve low-power, high-performance, or
This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.
This paper was presented Artificial Neural Network (ANN) as one of the predicting methods to obtain more accurate predicted data. Several methods have been applied to this purpose but still not gave a better accuracy. The method could be used in linear and nonlinear characteristic of data. Back propagation neural network was implemented in this experiment to solve the predicting problem. This research proposed to predict some future points to get the advantages from it. The data was demonstrated in generate from determinant of cement demand in Indonesia region. The data was used 6 variables which influenced the demand factors. The contribution of this experiment was an exploring the accuracy of predicted and the future points value of data. The result of this experiment was: data A, B, C, D, E and F data had the range of MSE 6.23e-9 until 1.34e-7. The MSE for the actual data was 2.39e-6 and the predicted was 1.99e-6. The predicting points has resulted on months 91 until 97 were 0.5854, 0.8448, 0.510, 0.6462, 0.8528, 0.516 and 0.5074 respectively. The delta between predicted and actual data were 0
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