High Prediction Accuracy and Low Error for ERP User Satisfaction by Hybrid of ANFIS and KNN Classification Pinky Kumawat Department of Computer Engineering Rajasthan College of Engineering for Women, Jaipur, RTU, Kota. kumawat.pinky3@gmail.com Abstract—Th
Abstract
dependent on Information Technology. Enterprise Resource
Planning (ERP) Systems are one of the most widely used latest
examples of Information Systems (IS) technology. They provide a
single window system to the organizations by integrating the
whole functions of them. Today, all enterprises are rapidly
adopted ERP systems. But, their adoption and implementation is
not being without any problem. The implementation process of
ERP is also a very challenging, time consuming and costly task.
Therefore, instead of many efforts if the implementation process
is failed. Then it will be a big failure for the organization. Hence,
to overcome this failure and increase the success rate of ERP
projects we need to develop a robust, reliable and accurate
predictor. This will help us to redirect the projects far better in
advance. The success of ERP systems depends on many factors.
US is one of the important factor among them. Hence, we develop
an efficient predictor of US using hybrid of ANFIS and KNN. We
were used this method first time in literature related to
prediction of US in ERP. The Hybrid method increases the
prediction accuracy more comparatively than previous reported
techniques ANN, ANFIS and KNN. The RMSE using Hybrid
method is 0.167629 and for KNN, ANFIS and ANN is 0.5,
0.486185, and 0.590329 respectively.
Full Text:
PDFReferences
Hossain, L. Patrick, J. David and Rashid, Mohammad. A., “Enterprise
Resource Planning: Global Opportunities & Challenges”, ISBN:
x, Idea Group Publishing, 2002.
https://www.infoq.com/articles/standish-chaos-2015.
A. R. Peslak and T. A. Boyle, “An exploratory study of the key skills
for entry-level ERP employees”, International Journal of Enterprise
Information Systems (IJEIS), Vol. 6, No. 2, pp. 1-14, 2010.
Maldonado, M., and Sierra, “User satisfaction as the foundation of the
success following an ERP adoption: An empirical study from Latin
America”, International Journal of Enterprise Information Systems
(IJEIS), Vol. 9, No. 3, pp. 77-99, 2013.
N. Garcia-Sanchez & L. E. Perez-Bernal, “Determination of critical
success factors in implementing an ERP systems: A field study in
Mexican enterprises”, Information Technology for Development, Vol.
, No. 3, pp. 293-309, 2007.
N. Gupta, G. Sharma, and R. S. Sharma, “A Comparative Study of ANFIS
Membership function to Predict ERP User Satisfaction using ANN and
MLRA”, International Journal of Computer Applications, Vol. 105-No. 5,
pp. 0975-8887, November 2014.
C. Venugopal, S. P. Devi and K. S. Rao, “Predicting ERP User
Satisfaction-an Adaptive Neuro Fuzzy Inference System (ANFIS)
Approach”, Intelligent Information Management, Vol. 2, pp. 420-430,
S. Dezdar, “User Satisfaction Issues in ERP Project”, World Academy of
Science, Engineering and Technology, International Journal of Social,
Behavioral, Economic, Business and Industrial Engineering, Vol. 6, No.
, pp. 2277-2280, 2012.
M. D. Frejik, and A. Powell, “User Satisfaction with ERP
Implementations: A Literature Review”, Association of Information
Systems, AIS Electronic Library (AISeL), MWAIS 2015 Proceedings,
Paper 25, pp. 1-4, 2015.
M. A. Lotfy, and L. Halawi, “A Conceptual Model to Measure ERP UserValue”, Issues in Information Systems, Vol. 16, Issue III, pp. 45-63, 2015.
R. Gupta, S. Chouhan, and N. Bhuria “Comparative study of Institute
based ERP based on ANFIS, ANN and MLRA”, International Journal of
Engineering and Technical Research (IJETR), Vol. 2, Issue-5, ISSN:
-0869, May 2014.
R. M. Bhawarkar, and L. P. Dhamande, “A Framework for the
Implemenation of Enterprise Resource Planning (ERP) to Improve the
Performance of Business”, International Journal of Research in Advent
Technology, Vol. 1, Issue 5, E-ISSN: 2321-9637, December 2013.
L. K. Roses, “Antecedents of End-User Satisfaction with an ERP System
in a Transactional Bank”, Journal of Information Systems and Technology
Management (IJSTEM), Vol.-8, no.-2, pp. 389-406, 2011.
S. Rouhani, and A. Z. Ravasan, “ERP success prediction: An artificial
neural network approach”, Scientia Iranica, Transactions E: Industrial
Engineering, Vol.-20, no.-3, pp. 992–1001, October 2012.
H. S. Jenatabadi, and A. Noudoostbeni, “End-User Satisfaction in ERP
System: Application of Logit Modeling”, Applied Mathematics Sciences,
Vol.-8, no.-24, pp. 1187-1192, February 2014.
Shih-Wen Chien, and S. M. Tsaur, “Investigating the Success of ERP
Systems: Case Studies in three Taiwanese High-Tech Industries”,
Computers in industry, Vol.-58, no. 8-9, pp. 783-793, March 2007.
P. Kumawat, and G. Kalani, “A Review on Prediction of ERP Outcome
Measurement and User Satisfaction by Use of AI (Fuzzy Logic and
Neural Networks) ”, International Journal of Engineering Research and
General Science, Vol. 3, Issue 4, ISSN 2091-2730, pp. 59-64, JulyAugust, 2015.
P. Kumawat, G. Kalani, N. K. Kumawat “Prediction of ERP Outcome
Measurement and User Satisfaction by using Adaptive Neuro Fuzzy
Inference System and SVM Classifiers approach” Proceedings of the
International Congress on Information and Communication Technology
(ICICT) in Springer, Vol. 1, pp. 229-237, (DOI 10.1007/978-981-10-
-5_25), 2015.
Haykin, S., “Neural Networks: A Comprehensive Foundation”, ISBN:
, 2nd Edition, Prentice-Hall, Upper Saddle River, NJ, 1999.
C. G. Dasgupta, G. S. Dispensa, and S. Ghose, ‘‘Comparing the Predictive
Performance of a Neural Network Model with Some Traditional Market
Response Models”, International Journal of Forecasting, Vol.-10, no.-2,
pp. 235–244, September 1994.
J. S. R. Jang, “Adaptive-Network-Based Fuzzy Inference system”, IEEE
Transactions on Systems, Man, and Cybernetics, Vol.-23, no.-3, pp. 665-
, June 1993.
G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “KNN Model-Based
Approach in Classification”, Lecture Notes in Computer Science, Vol.-
, pp. 986 – 996, November 2003.
P. Kumawat and N. K. Kumawat, “Investigating End User Satisfaction in
ERP Systems: An Analytical Approach”, International Journal on
Emerging Trends in Technology (IJETT), Vol. 4, ISSN: 2455 – 0124,
Issue 3, pp. 9020-9024, Sept 2017.
Refbacks
- There are currently no refbacks.
Copyright © IJETT, International Journal on Emerging Trends in Technology