This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.
NOx is one of emission pollutant resulted from Backhoe equipment. This research aims to build a predictive model to estimate NOx pollutant released by backhoe equipment using Support Vector Machine model. Two type of kernel types (radial basis function and linear kernel types) are compared. The study runs the model several time to maximize the accuracy of SVM by finding the optimized parameter, which includes C, ε, and γ. The results show that radial basis function kernel type provide higher accuracy than linear kernel type. In addition, this study also conclude that higher C and γ parameter results in much lower mean absolute error value. However, it requires much longer calculation time. The SVM predictive model also show that the significant factors to predict NOx emission are MAP, RPM, backhoe type and the intake temperature.
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