k近邻法与朴素贝叶斯法在贷款资格分类中的比较

Perlius Septria, A. Asnawati, Jhoanne Fredricka
{"title":"k近邻法与朴素贝叶斯法在贷款资格分类中的比较","authors":"Perlius Septria, A. Asnawati, Jhoanne Fredricka","doi":"10.53697/jkomitek.v2i2.952","DOIUrl":null,"url":null,"abstract":"The Kemala Aman Finance Bengkulu Cooperative also provides loan facilities for consumers, but not all loan applications will be approved. So far, the determination of the feasibility of applying for a loan is seen from several aspects including marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, number of loan applications, length of loan application. These aspects are analyzed manually by the survey team by filling out the form provided, and then the survey results are given to superiors to be followed up on whether the loan application is accepted or rejected. The loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative is used to make it easier to determine the eligibility of prospective borrowers to be given loans based on marital status, number of dependents, age, last education, occupation, income, home ownership, collateral, number of loan applications, length of loan application. This application is made using the Visual Basic .Net programming language which can be accessed by the Kemala Aman Finance Bengkulu Cooperative Admin. Comparative analysis of the K-Nearest Neighbor method and the Naive Bayes method was carried out by looking at the level of accuracy by comparing the classification results of the two methods with the real data from the classification results obtained from the Kemala Aman Finance Cooperative Bengkulu. Based on the processing time, the KNN method is faster than the Naive Bayes method. Based on the level of accuracy, the KNN method has the highest level of accuracy compared to the Naive Bayes method. Based on the tests that have been carried out, the functionality of the loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative runs as expected, and the application is able to display the results of the classification of loan eligibility through the KNN Method and the Naive Bayes Method.","PeriodicalId":371693,"journal":{"name":"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of the K-Nearest Neighbor Method and the Naive Bayes Method in Classification of Eligibility for Lending\",\"authors\":\"Perlius Septria, A. Asnawati, Jhoanne Fredricka\",\"doi\":\"10.53697/jkomitek.v2i2.952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Kemala Aman Finance Bengkulu Cooperative also provides loan facilities for consumers, but not all loan applications will be approved. So far, the determination of the feasibility of applying for a loan is seen from several aspects including marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, number of loan applications, length of loan application. These aspects are analyzed manually by the survey team by filling out the form provided, and then the survey results are given to superiors to be followed up on whether the loan application is accepted or rejected. The loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative is used to make it easier to determine the eligibility of prospective borrowers to be given loans based on marital status, number of dependents, age, last education, occupation, income, home ownership, collateral, number of loan applications, length of loan application. This application is made using the Visual Basic .Net programming language which can be accessed by the Kemala Aman Finance Bengkulu Cooperative Admin. Comparative analysis of the K-Nearest Neighbor method and the Naive Bayes method was carried out by looking at the level of accuracy by comparing the classification results of the two methods with the real data from the classification results obtained from the Kemala Aman Finance Cooperative Bengkulu. Based on the processing time, the KNN method is faster than the Naive Bayes method. Based on the level of accuracy, the KNN method has the highest level of accuracy compared to the Naive Bayes method. Based on the tests that have been carried out, the functionality of the loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative runs as expected, and the application is able to display the results of the classification of loan eligibility through the KNN Method and the Naive Bayes Method.\",\"PeriodicalId\":371693,\"journal\":{\"name\":\"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)\",\"volume\":\"137 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53697/jkomitek.v2i2.952\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Komputer, Informasi dan Teknologi (JKOMITEK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53697/jkomitek.v2i2.952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

Kemala Aman Finance Bengkulu合作社也为消费者提供贷款便利,但并非所有贷款申请都将获得批准。到目前为止,申请贷款的可行性的确定是从几个方面来看的,包括婚姻状况、受抚养人数、年龄、最后的教育程度、职业、月收入、住房所有权、抵押品、贷款申请次数、贷款申请期限。这些方面由调查小组通过填写提供的表格进行人工分析,然后将调查结果交给上级,由上级跟踪贷款申请是被接受还是被拒绝。Kemala Aman金融Bengkulu合作社的贷款资格申请是为了更容易地根据婚姻状况、家属人数、年龄、上次教育程度、职业、收入、住房所有权、抵押品、贷款申请数量、贷款申请期限等因素确定潜在借款人获得贷款的资格。本应用程序是使用Visual Basic . net编程语言编写的,可由Kemala Aman Finance Bengkulu合作社管理员访问。对k近邻法和朴素贝叶斯法进行对比分析,将两种方法的分类结果与本库鲁Kemala Aman金融合作社分类结果的真实数据进行对比,从准确率水平上进行对比分析。从处理时间来看,KNN方法比朴素贝叶斯方法更快。基于准确率水平,与朴素贝叶斯方法相比,KNN方法具有最高的准确率水平。根据已进行的测试,Kemala Aman金融Bengkulu合作社的贷款资格申请功能运行正常,该应用程序能够显示通过KNN方法和朴素贝叶斯方法对贷款资格进行分类的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Comparison of the K-Nearest Neighbor Method and the Naive Bayes Method in Classification of Eligibility for Lending
The Kemala Aman Finance Bengkulu Cooperative also provides loan facilities for consumers, but not all loan applications will be approved. So far, the determination of the feasibility of applying for a loan is seen from several aspects including marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, number of loan applications, length of loan application. These aspects are analyzed manually by the survey team by filling out the form provided, and then the survey results are given to superiors to be followed up on whether the loan application is accepted or rejected. The loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative is used to make it easier to determine the eligibility of prospective borrowers to be given loans based on marital status, number of dependents, age, last education, occupation, income, home ownership, collateral, number of loan applications, length of loan application. This application is made using the Visual Basic .Net programming language which can be accessed by the Kemala Aman Finance Bengkulu Cooperative Admin. Comparative analysis of the K-Nearest Neighbor method and the Naive Bayes method was carried out by looking at the level of accuracy by comparing the classification results of the two methods with the real data from the classification results obtained from the Kemala Aman Finance Cooperative Bengkulu. Based on the processing time, the KNN method is faster than the Naive Bayes method. Based on the level of accuracy, the KNN method has the highest level of accuracy compared to the Naive Bayes method. Based on the tests that have been carried out, the functionality of the loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative runs as expected, and the application is able to display the results of the classification of loan eligibility through the KNN Method and the Naive Bayes Method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Information System Of Student Point Violations (Case Study Of Sma Muhammadiyah 7 Serbelawan) Hotspot Network Security System From Brute Force Attack Using Pfsense External Firewall (Case Study of Wifi-Ku.Net Hotspot) Application Of 3-Dimensional Modeling In Android-Based Adventure Game Applications Application Of Data Mining Using The Naïve Bayes Classification Method To Predict Public Interest Participation In The 2024 Elections Graduation Book Information System Of Muhammadyah University Of Bengkulu
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1