Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm
Credit is one of the modern economic behaviors. In practice, credit can be either borrowing a certain amount of money or purchasing goods with a gradual payment process and within an agreed timeframe. Economic conditions that are less supportive and high community needs make people choose to buy go...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Islamic Economics and Business - Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan
2017-10-01
|
| Series: | International Journal of Islamic Business and Economics (IJIBEC) |
| Subjects: | |
| Online Access: | http://e-journal.iainpekalongan.ac.id/index.php/IJIBEC/article/view/882 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850160348759326720 |
|---|---|
| author | Ivandari Ivandari Tria Titiani Chasanah Sattriedi Wahyu Binabar M. Adib Al Karomi |
| author_facet | Ivandari Ivandari Tria Titiani Chasanah Sattriedi Wahyu Binabar M. Adib Al Karomi |
| author_sort | Ivandari Ivandari |
| collection | DOAJ |
| description |
Credit is one of the modern economic behaviors. In practice, credit can be either borrowing a certain amount of money or purchasing goods with a gradual payment process and within an agreed timeframe. Economic conditions that are less supportive and high community needs make people choose to buy goods with this credit process. Unfortunately the high needs sometimes are not in line with the ability to make payments in accordance with the initial agreement. Such condition causes the payment process to be disrupted or also called the term “bad creditâ€. This research uses public data of credit card dataset from UCI repository and private data that is dataset of credit approval from local banking. The information gain algorithm is used to calculate the weights of each of the attributes. From the calculation results note that all attributes have different weights. This study resulted in the conclusion that not all data attributes influence the classification result. Suppose attribute A1 to UCI dataset as well as loan type attribute on local dataset that has information gain weight 0 (zero). The result of classification using K-Nearest Neighbors algorithm shows that there is an increase of 7.53% for UCI dataset and 3.26% for local dataset after feature selection on both datasets.
|
| format | Article |
| id | doaj-art-771b8989ea0c4021b4cfccf96a04d434 |
| institution | OA Journals |
| issn | 2599-3216 2615-420X |
| language | English |
| publishDate | 2017-10-01 |
| publisher | Faculty of Islamic Economics and Business - Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan |
| record_format | Article |
| series | International Journal of Islamic Business and Economics (IJIBEC) |
| spelling | doaj-art-771b8989ea0c4021b4cfccf96a04d4342025-08-20T02:23:11ZengFaculty of Islamic Economics and Business - Universitas Islam Negeri K.H. Abdurrahman Wahid PekalonganInternational Journal of Islamic Business and Economics (IJIBEC)2599-32162615-420X2017-10-011110.28918/ijibec.v1i1.882882Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor AlgorithmIvandari Ivandari0Tria Titiani Chasanah1Sattriedi Wahyu Binabar2M. Adib Al Karomi3STMIK Widya Pratama Pekalongan, IndonesiaSTIMIK Widya Pratama PekalonganSTMIK Widya Pratama PekalonganSTMIK Widya Pratama Pekalongan Credit is one of the modern economic behaviors. In practice, credit can be either borrowing a certain amount of money or purchasing goods with a gradual payment process and within an agreed timeframe. Economic conditions that are less supportive and high community needs make people choose to buy goods with this credit process. Unfortunately the high needs sometimes are not in line with the ability to make payments in accordance with the initial agreement. Such condition causes the payment process to be disrupted or also called the term “bad creditâ€. This research uses public data of credit card dataset from UCI repository and private data that is dataset of credit approval from local banking. The information gain algorithm is used to calculate the weights of each of the attributes. From the calculation results note that all attributes have different weights. This study resulted in the conclusion that not all data attributes influence the classification result. Suppose attribute A1 to UCI dataset as well as loan type attribute on local dataset that has information gain weight 0 (zero). The result of classification using K-Nearest Neighbors algorithm shows that there is an increase of 7.53% for UCI dataset and 3.26% for local dataset after feature selection on both datasets. http://e-journal.iainpekalongan.ac.id/index.php/IJIBEC/article/view/882KNN AccuracyFeature selectionfeature selectionCredit Approval |
| spellingShingle | Ivandari Ivandari Tria Titiani Chasanah Sattriedi Wahyu Binabar M. Adib Al Karomi Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm International Journal of Islamic Business and Economics (IJIBEC) KNN Accuracy Feature selection feature selection Credit Approval |
| title | Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm |
| title_full | Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm |
| title_fullStr | Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm |
| title_full_unstemmed | Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm |
| title_short | Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm |
| title_sort | data attribute selection with information gain to improve credit approval classification performance using k nearest neighbor algorithm |
| topic | KNN Accuracy Feature selection feature selection Credit Approval |
| url | http://e-journal.iainpekalongan.ac.id/index.php/IJIBEC/article/view/882 |
| work_keys_str_mv | AT ivandariivandari dataattributeselectionwithinformationgaintoimprovecreditapprovalclassificationperformanceusingknearestneighboralgorithm AT triatitianichasanah dataattributeselectionwithinformationgaintoimprovecreditapprovalclassificationperformanceusingknearestneighboralgorithm AT sattriediwahyubinabar dataattributeselectionwithinformationgaintoimprovecreditapprovalclassificationperformanceusingknearestneighboralgorithm AT madibalkaromi dataattributeselectionwithinformationgaintoimprovecreditapprovalclassificationperformanceusingknearestneighboralgorithm |