Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

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 overcom...

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Main Authors: Maulida Ayu Fitriani, Dany Candra Febrianto
Format: Article
Language:Indonesian
Published: Universitas Muhammadiyah Purwokerto 2021-05-01
Series:Jurnal Informatika
Subjects:
Online Access:http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/7983
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author Maulida Ayu Fitriani
Dany Candra Febrianto
author_facet Maulida Ayu Fitriani
Dany Candra Febrianto
author_sort Maulida Ayu Fitriani
collection DOAJ
description 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%.
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spelling doaj.art-b9687cfa077d48e490e46e0fcdaf166f2022-12-21T18:46:45ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012021-05-0191253210.30595/juita.v9i1.79833370Data Mining for Potential Customer Segmentation in the Marketing Bank DatasetMaulida Ayu Fitriani0Dany Candra Febrianto1Universitas Muhammadiyah PurwokertoUniversitas Gadjah MadaDirect 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%.http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/7983data mining, bank marketing, smote
spellingShingle Maulida Ayu Fitriani
Dany Candra Febrianto
Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset
Jurnal Informatika
data mining, bank marketing, smote
title Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset
title_full Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset
title_fullStr Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset
title_full_unstemmed Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset
title_short Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset
title_sort data mining for potential customer segmentation in the marketing bank dataset
topic data mining, bank marketing, smote
url http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/7983
work_keys_str_mv AT maulidaayufitriani dataminingforpotentialcustomersegmentationinthemarketingbankdataset
AT danycandrafebrianto dataminingforpotentialcustomersegmentationinthemarketingbankdataset