Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

Maulida Ayu Fitriani; Dany Candra Febrianto

doi:10.30595/juita.v9i1.7983

Authors

Maulida Ayu Fitriani Universitas Muhammadiyah Purwokerto
Dany Candra Febrianto Universitas Gadjah Mada

DOI:

https://doi.org/10.30595/juita.v9i1.7983

Keywords:

data mining, bank marketing, SMOTE

Abstract

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 NaÃ¯ve 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%.

Author Biographies

Maulida Ayu Fitriani, Universitas Muhammadiyah Purwokerto

Teknik Informatika

Dany Candra Febrianto, Universitas Gadjah Mada

Departemen Teknik Elektro dan Teknologi Informasi

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