Jurnal Informatika (May 2021)

Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

  • Maulida Ayu Fitriani,
  • Dany Candra Febrianto

DOI
https://doi.org/10.30595/juita.v9i1.7983
Journal volume & issue
Vol. 9, no. 1
pp. 25 – 32

Abstract

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Direct marketing is an effort made by the Bank to increase sales of its products and services, but the Bank sometimes has to contact a customer or prospective customer more than once to ascertain whether the customer or prospective customer is willing to subscribe to a product or service. To overcome this ineffective process several data mining methods are proposed. This study compares several data mining methods such as Naïve Bayes, K-NN, Random Forest, SVM, J48, AdaBoost J48 which prior to classification the SMOTE pre-processing technique was done in order to eliminate the class imbalance problem in the Bank Marketing dataset instance. The SMOTE + Random Forest method in this study produced the highest accuracy value of 92.61%.

Keywords