The Use of Data Mining for Prediction of Customer Loyalty
Abstract: This article
discusses the analysis
of customer loyalty
using three data
mining methods: C4.5,Naive Bayes, and Nearest Neighbor
Algorithms and real-world empirical data.
The data contain
ten attributes related to the
customer loyalty and are obtained from a national multimedia
company in Indonesia.
The dataset contains 2269
records. The study also evaluates the effects of the
size of the
training data to
the accuracy of the
classification. The results
suggest that C4.5
algorithm produces highest
classification accuracy at the order of
81% followed by the methods
of Naive Bayes
76% and Nearest Neighbor
55%. In addition,
the numerical evaluation also
suggests that the
proportion of 80% is
optimal for the
training set.
Keywords: Customer loyalty;
Attribute analysis; C4.5; Naiv¨e Bayes; Nearest Neighbor Algorithmghbor
algorithms
Author: Andri Wijaya, Abba
Suganda Girsang
Journal Code: jptinformatikagg160020
