ANALISIS DISKRIMINAN, REGRESI LOGISTIK, NEURAL NETWORK DAN MARS PADA PENGKLASIFIKASIAN DATA

  • Thomas Pentury Jurusan Matematika FMIPA Universitas Pattimura
Keywords: Discriminant Analysis, Logistic Regression, Neural Network, Multivariate Adaptive Regression Spline

Abstract

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

Downloads

Download data is not yet available.

References

Agresti, A. (1990). Categorical Data Analysis. NY: John Wiley and Sons, Inc
Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C. J. (1993). Classification and Regression Trees. CA: Wadsworth
Chikolwa, B. (2007). An Emprical Analysis of Commercial Mortgage-Backed Securities Credit Rating. Australian Evidence, 13th Pacific-Rim Real Estate Society Conference Premantle. Western Australia
Dillon, W.R., & Goldstein, M. (1984). Multivariate Analysis Methods and Application. NY: John Wiley and Sons, Inc
Demaris, A. (1995). A Tutorial in Logistic Regression. Journal of Marriage and the Family, 57:956-968
Friedman, J.H. (1991). Multivariate Adaptive Regression Splines. The Annals of Statistics, Vol. 19, No. 1, 1-67
Hawkins, D.M., & McLachlan, G.J. (1997). High-Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, Vol. 92, No. 437, 136-143
Hermawan, A. (2006). Jaringan Syaraf Tiruan Teori dan Aplikasinya. Yogyakarta: Andi
Hosmer, D.W., & Lemeshow, S. (1989). Applied Logistic Regression. NY: John Wiley and Sons, Inc
Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., & Wu, S. (2003). Credit Analysis With Support Vector Machines and Neural Network : a Market Comparative Study, Decision Support System 37. Elsevier, 543-558
Huberty, C. J. (1984). Issues in the Use and Interpretation of Discriminant Analysis. Psychological Bulletin, 95:156-171
Iriawan, N., & Astuti, S.P. (2006). Mengolah Data Statistik Dengan Mudah Menggunakan MINITAB 14. Yogyakarta: Andi
Johnson, R.A., & Wichern, D.W. (1992). Applied Multivariate Statistical Analysis. NJ: Prentice-Hall
Le, C.T. (1998). Applied Categorical data Analysis. New York: John Wiley and Sons, Inc
Morrison, D.G. (1969). On the Interpretation of Discriminant Analysis. Journal of Marketing Research, 6(2):156-163
Sharma, S. (1996). Applied Multivariate Techniques. NY: John Wiley and Sons, Inc
Published
2007-12-01
How to Cite
[1]
T. Pentury, “ANALISIS DISKRIMINAN, REGRESI LOGISTIK, NEURAL NETWORK DAN MARS PADA PENGKLASIFIKASIAN DATA”, BAREKENG: J. Il. Mat. & Ter., vol. 1, no. 2, pp. 8-13, Dec. 2007.