Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers
The banking sector faces significant challenges in effectively promoting its products and services. While direct marketing has proven to be a potent tool for customer acquisition, it often leads to customer dissatisfaction, thereby tarnishing the bank's reputation. Leveraging Business Intellig...
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| Format: | Article |
| Language: | English |
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2024-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
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| Online Access: | https://journal.yrpipku.com/index.php/jaets/article/view/5974 |
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| author | Firman Aziz Mutia Maulida Jafar Jafar Nurafni Shahnyb Norma Nasir Ampauleng Ampauleng |
| author_facet | Firman Aziz Mutia Maulida Jafar Jafar Nurafni Shahnyb Norma Nasir Ampauleng Ampauleng |
| author_sort | Firman Aziz |
| collection | DOAJ |
| description |
The banking sector faces significant challenges in effectively promoting its products and services. While direct marketing has proven to be a potent tool for customer acquisition, it often leads to customer dissatisfaction, thereby tarnishing the bank's reputation. Leveraging Business Intelligence (BI) technology offers a strategic advantage by enabling the classification and analysis of customer data, particularly for time deposit customers. This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. The proposed Ensemble Least Squares Support Vector Machine (ELS-SVM) algorithm demonstrated superior performance compared to traditional SVM and LS-SVM methods. Notably, the ELS-SVM achieved an average performance improvement of 10.04% over standard Support Vector Machine (SVM) techniques.
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| format | Article |
| id | doaj-art-3e6d986e0d0b4e06b878fd765dd944d5 |
| institution | DOAJ |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-3e6d986e0d0b4e06b878fd765dd944d52025-08-20T02:49:10ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792024-12-016110.37385/jaets.v6i1.5974Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit CustomersFirman Aziz0Mutia Maulida1Jafar Jafar2Nurafni Shahnyb3Norma Nasir4Ampauleng Ampauleng5Universitas Pancasakti MakassarUniversitas Lambung MangkuratPancasakti University, MakassarPancasakti University, MakassarUniversitas Negeri MakassarSTIEM Bongaya The banking sector faces significant challenges in effectively promoting its products and services. While direct marketing has proven to be a potent tool for customer acquisition, it often leads to customer dissatisfaction, thereby tarnishing the bank's reputation. Leveraging Business Intelligence (BI) technology offers a strategic advantage by enabling the classification and analysis of customer data, particularly for time deposit customers. This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. The proposed Ensemble Least Squares Support Vector Machine (ELS-SVM) algorithm demonstrated superior performance compared to traditional SVM and LS-SVM methods. Notably, the ELS-SVM achieved an average performance improvement of 10.04% over standard Support Vector Machine (SVM) techniques. https://journal.yrpipku.com/index.php/jaets/article/view/5974Business IntelligenceBank MarketingClassificationPotential Deposits CustomersEnsemble Least Square Support Vector Machine |
| spellingShingle | Firman Aziz Mutia Maulida Jafar Jafar Nurafni Shahnyb Norma Nasir Ampauleng Ampauleng Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers Journal of Applied Engineering and Technological Science Business Intelligence Bank Marketing Classification Potential Deposits Customers Ensemble Least Square Support Vector Machine |
| title | Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers |
| title_full | Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers |
| title_fullStr | Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers |
| title_full_unstemmed | Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers |
| title_short | Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers |
| title_sort | development of an optimized ensemble least squares model for identifying potential deposit customers |
| topic | Business Intelligence Bank Marketing Classification Potential Deposits Customers Ensemble Least Square Support Vector Machine |
| url | https://journal.yrpipku.com/index.php/jaets/article/view/5974 |
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