ANALISIS DISKRIMINAN, REGRESI LOGISTIK, NEURAL NETWORK DAN MARS PADA PENGKLASIFIKASIAN DATA - Pentury, Thomas (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2007)
The purpose of this research is to apply and compare the discriminant analysis, logistic regression, Neural Network (NN) and Multivariate Adaptive Regression Spline (MARS) at HBAT and IRIS data. Next will be the classification of fourth methods using the SPSS statistical software, MINITAB, MARS, and R. The results showed that the data HBAT predictor variables affected to the response variable which is the quality of the product (X6), Complaint resolution (X9) and Salesforce image (X12), whereas all predictor variables on the IRIS data affect the response variable. A more precise method used in HBAT data classification is NN and discriminant analysis because the value of the resulting classification accuracy is greater, especially for testing. While a more precise method used in the IRIS data classification is discriminant analysis because the value of the resulting classification accuracy is greater