Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level
The amount of data from credit card users is increasing from year to year. Credit cards are an important need for people to make payments. The increasing number of credit card users is because it is considered more effective and efficient. The third method used today has a function to determine the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Politeknik Negeri Batam
2023-12-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/5790 |
| _version_ | 1797397368812863488 |
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| author | Hafid Ramadhan Mohammad Rizal Abdan Kamaludin Muhammad Alfan Nasrullah Dwi Rolliawati |
| author_facet | Hafid Ramadhan Mohammad Rizal Abdan Kamaludin Muhammad Alfan Nasrullah Dwi Rolliawati |
| author_sort | Hafid Ramadhan |
| collection | DOAJ |
| description | The amount of data from credit card users is increasing from year to year. Credit cards are an important need for people to make payments. The increasing number of credit card users is because it is considered more effective and efficient. The third method used today has a function to determine the effective outcome of credit card user scenarios. In this study, a comparison was made using the Hierarchical Clustering, K-Means and DBSCAN methods to determine the results of credit card customer segmentation analysis to be used as a market strategy. The results obtained based on the best silhouette coefficient score method is two cluster hierarchical clustering with 0.82322 score. Based on the best mean value customers are divided into two segments, and it is suggested to develop strategies for both segments. |
| first_indexed | 2024-03-09T01:08:52Z |
| format | Article |
| id | doaj.art-afed33e3205c44bd88163a26007a22be |
| institution | Directory Open Access Journal |
| issn | 2548-6861 |
| language | English |
| last_indexed | 2024-03-09T01:08:52Z |
| publishDate | 2023-12-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj.art-afed33e3205c44bd88163a26007a22be2023-12-11T08:06:22ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612023-12-017224625110.30871/jaic.v7i2.57905790Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure LevelHafid RamadhanMohammad Rizal Abdan KamaludinMuhammad Alfan NasrullahDwi RolliawatiThe amount of data from credit card users is increasing from year to year. Credit cards are an important need for people to make payments. The increasing number of credit card users is because it is considered more effective and efficient. The third method used today has a function to determine the effective outcome of credit card user scenarios. In this study, a comparison was made using the Hierarchical Clustering, K-Means and DBSCAN methods to determine the results of credit card customer segmentation analysis to be used as a market strategy. The results obtained based on the best silhouette coefficient score method is two cluster hierarchical clustering with 0.82322 score. Based on the best mean value customers are divided into two segments, and it is suggested to develop strategies for both segments.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/5790clusteringcredit cardcomparisonsegmentationsilhouette coefficient |
| spellingShingle | Hafid Ramadhan Mohammad Rizal Abdan Kamaludin Muhammad Alfan Nasrullah Dwi Rolliawati Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level Journal of Applied Informatics and Computing clustering credit card comparison segmentation silhouette coefficient |
| title | Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level |
| title_full | Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level |
| title_fullStr | Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level |
| title_full_unstemmed | Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level |
| title_short | Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level |
| title_sort | comparison of hierarchical k means and dbscan clustering methods for credit card customer segmentation analysis based on expenditure level |
| topic | clustering credit card comparison segmentation silhouette coefficient |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/5790 |
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