{"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. 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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. 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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。
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.