Use of Data Mining for Prediction of Customer Loyalty

Andri Wijaya; Abba Suganda Girsang

doi:10.21512/commit.v10i1.1660

Authors

Andri Wijaya Bina Nusantara University
Abba Suganda Girsang Bina Nusantara University

DOI:

https://doi.org/10.21512/commit.v10i1.1660

Keywords:

Customer loyalty, Attribute analysis, C4.5, Naiv¨e Bayes, Nearest Neighbor Algorithmghbor algorithms

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.

Dimensions

Plum Analytics

Author Biographies

Andri Wijaya, Bina Nusantara University

Master of Information Technology

Abba Suganda Girsang, Bina Nusantara University

Master of Information Technology

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