Applied of Classification Technique in Data Mining For Credit Scoring
In the development of the banking business, credit issues remain interesting to study and uncover. Most of the problems occur not in the system implemented by the bank, but the problem occurs precisely in the human resources who manage credit, either in their relationship with consumers or in errors...
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| Format: | Article |
| Language: | English |
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Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat
2022-12-01
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| Series: | Inspiration |
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| Online Access: | https://ojs.unitama.ac.id/index.php/inspiration/article/view/17 |
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| author | Heriyanto Heriyanto Ika Kurniawati Fachri Amsury Muhammad Rizki Fahdia Irwansyah Saputra Nanang Ruhyana Asrul |
| author_facet | Heriyanto Heriyanto Ika Kurniawati Fachri Amsury Muhammad Rizki Fahdia Irwansyah Saputra Nanang Ruhyana Asrul |
| author_sort | Heriyanto Heriyanto |
| collection | DOAJ |
| description | In the development of the banking business, credit issues remain interesting to study and uncover. Most of the problems occur not in the system implemented by the bank, but the problem occurs precisely in the human resources who manage credit, either in their relationship with consumers or in errors on the part of the bank which mispredicts in assessing consumers who apply for credit. Several studies in the computer field have been carried out to reduce credit risk which causes losses to the company. In this study, a comparison of the Naive Bayes, C4.5 and KNN algorithms was carried out which was applied to consumer data that received credit eligibility for good and bad customers. The best prediction results are nave Bayes with an accuracy of 95.95 % and an AUC of 0.974. The results of this classification are implemented in the form of a website-based application that can be used to facilitate related parties in the credit scoring system. |
| format | Article |
| id | doaj-art-888dc8d33602456686292c03949324b8 |
| institution | Kabale University |
| issn | 2088-6705 2621-5608 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat |
| record_format | Article |
| series | Inspiration |
| spelling | doaj-art-888dc8d33602456686292c03949324b82025-01-28T05:31:39ZengUniversitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian MasyarakatInspiration2088-67052621-56082022-12-011229710410.35585/inspir.v12i2.1717Applied of Classification Technique in Data Mining For Credit ScoringHeriyanto Heriyanto0Ika Kurniawati1Fachri Amsury2Muhammad Rizki Fahdia3Irwansyah Saputra4Nanang Ruhyana5Asrul6Universitas Nusa MandiriUniversitas Nusa MandiriUniversitas Nusa MandiriUniversitas Nusa MandiriUniversitas Nusa MandiriUniversitas Nusa MandiriUniversitas Teknologi Akba MakassarIn the development of the banking business, credit issues remain interesting to study and uncover. Most of the problems occur not in the system implemented by the bank, but the problem occurs precisely in the human resources who manage credit, either in their relationship with consumers or in errors on the part of the bank which mispredicts in assessing consumers who apply for credit. Several studies in the computer field have been carried out to reduce credit risk which causes losses to the company. In this study, a comparison of the Naive Bayes, C4.5 and KNN algorithms was carried out which was applied to consumer data that received credit eligibility for good and bad customers. The best prediction results are nave Bayes with an accuracy of 95.95 % and an AUC of 0.974. The results of this classification are implemented in the form of a website-based application that can be used to facilitate related parties in the credit scoring system.https://ojs.unitama.ac.id/index.php/inspiration/article/view/17c4.5credit scoringdata miningk-nnnaïve bayes |
| spellingShingle | Heriyanto Heriyanto Ika Kurniawati Fachri Amsury Muhammad Rizki Fahdia Irwansyah Saputra Nanang Ruhyana Asrul Applied of Classification Technique in Data Mining For Credit Scoring Inspiration c4.5 credit scoring data mining k-nn naïve bayes |
| title | Applied of Classification Technique in Data Mining For Credit Scoring |
| title_full | Applied of Classification Technique in Data Mining For Credit Scoring |
| title_fullStr | Applied of Classification Technique in Data Mining For Credit Scoring |
| title_full_unstemmed | Applied of Classification Technique in Data Mining For Credit Scoring |
| title_short | Applied of Classification Technique in Data Mining For Credit Scoring |
| title_sort | applied of classification technique in data mining for credit scoring |
| topic | c4.5 credit scoring data mining k-nn naïve bayes |
| url | https://ojs.unitama.ac.id/index.php/inspiration/article/view/17 |
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