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

Pinky Kumawat

Abstract


The incoming era is becoming more friendly and
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.

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