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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Format: | Article |
Language: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2021-05-01
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Series: | Jurnal Informatika |
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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%. |
first_indexed | 2024-12-21T23:23:22Z |
format | Article |
id | doaj.art-b9687cfa077d48e490e46e0fcdaf166f |
institution | Directory Open Access Journal |
issn | 2086-9398 2579-8901 |
language | Indonesian |
last_indexed | 2024-12-21T23:23:22Z |
publishDate | 2021-05-01 |
publisher | Universitas Muhammadiyah Purwokerto |
record_format | Article |
series | Jurnal Informatika |
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 |