Classification of Customer Loans Using Hybrid Data Mining
At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does no...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2022-05-01
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| Series: | Jurnal Informatika |
| Subjects: | |
| Online Access: | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/12521 |
| _version_ | 1811324876179374080 |
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| author | Eka Praja Wiyata Mandala Eva Rianti Sarjon Defit |
| author_facet | Eka Praja Wiyata Mandala Eva Rianti Sarjon Defit |
| author_sort | Eka Praja Wiyata Mandala |
| collection | DOAJ |
| description | At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the "Not Feasible" cluster, 15 customers into the "Feasible" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%. |
| first_indexed | 2024-04-13T14:22:00Z |
| format | Article |
| id | doaj.art-a6ea7c96b9ad4d4d91f158a9438e4d10 |
| institution | Directory Open Access Journal |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| last_indexed | 2024-04-13T14:22:00Z |
| publishDate | 2022-05-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj.art-a6ea7c96b9ad4d4d91f158a9438e4d102022-12-22T02:43:25ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012022-05-01101455210.30595/juita.v10i1.125214475Classification of Customer Loans Using Hybrid Data MiningEka Praja Wiyata Mandala0Eva Rianti1Sarjon Defit2Universitas Putra Indonesia YPTK PadangUniversitas Putra Indonesia YPTK PadangUniversitas Putra Indonesia YPTK PadangAt this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the "Not Feasible" cluster, 15 customers into the "Feasible" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%.http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/12521clusteringclassificationcustomer loanshybrid data mining |
| spellingShingle | Eka Praja Wiyata Mandala Eva Rianti Sarjon Defit Classification of Customer Loans Using Hybrid Data Mining Jurnal Informatika clustering classification customer loans hybrid data mining |
| title | Classification of Customer Loans Using Hybrid Data Mining |
| title_full | Classification of Customer Loans Using Hybrid Data Mining |
| title_fullStr | Classification of Customer Loans Using Hybrid Data Mining |
| title_full_unstemmed | Classification of Customer Loans Using Hybrid Data Mining |
| title_short | Classification of Customer Loans Using Hybrid Data Mining |
| title_sort | classification of customer loans using hybrid data mining |
| topic | clustering classification customer loans hybrid data mining |
| url | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/12521 |
| work_keys_str_mv | AT ekaprajawiyatamandala classificationofcustomerloansusinghybriddatamining AT evarianti classificationofcustomerloansusinghybriddatamining AT sarjondefit classificationofcustomerloansusinghybriddatamining |