用于商业信息系统的未来债务人可行性分类的算法C4.5

Raymond Sutjiadi, Titasari Rahmawati, Anindya Ayu Prahartiwi
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引用次数: 0

摘要

在信息技术时代,互联网可以很容易地作为一种媒介来销售商品。通过使用基于网络的信息系统,可以利用市场覆盖,获得客户的可能性更大。在交易中,特别是在零售市场中,购买过程通常使用信用支付系统进行。当然,为了批准信贷申请,需要从债权人到债务人的详细评估,以保证支付的连续性。本研究采用C4.5算法开发了一个集成了信誉度判定特征的销售信息系统。在这个系统中,客户可以购买和申请信贷。管理员可以通过将信用可信度视为C.45算法处理的结果来拒绝或接受信用申请过程。通过使用此功能,可以根据个人资料资格(如职业类型、家属数量和住所状态)确定债务人是否有资格获得信贷。采用增量模型的方法开发信息系统。选择此方法是为了根据紧急程度对精加工系统进行优先排序,并将其分成几个子过程,即销售信息系统、帐户管理、采购和管理。采用黑盒测试方法对系统的实现结果进行了测试,黑盒测试方法被认为是对系统进行全面测试的最有效方法。同时,从信誉度判定特征的测试结果中,得到精密度(Precision) = 0.876,召回率(Recall) = 0.952, F-Measure = 0.912的平均值的准确结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algoritma C4.5 Untuk Klasifikasi Kelayakan Kredit Calon Debitur Pada Sistem Informasi Penjualan
In the era of information technology, the internet can be used as a medium to sell goods easily. By using the web-based information system, market coverage can be leveraged and the probability of getting customers is bigger. In a transaction, especially in the retail market, the process of buying is usually carried out using a credit payment system. Of course, in order to approve credit application is required scrutiny assessment from creditor to debtor profile to guarantee the payment continuity. In this research is developed a sales information system, which is integrated with creditworthiness determination feature using C4.5 algorithm. In this system, customers are able to buy and also apply credit. The credit application process can be rejected or accepted by the admin by looking at the credit trustworthiness as the result of C.45 algorithm process. By using this feature can be determined whether a debtor is qualified or not to receive credit, based on profile qualification such as occupation type, number of dependents, and domicile status. To develop information systems is used the method of Incremental Model. This method is chosen to prioritize the finishing system by its urgency, separated into several sub-processes, i.e., sales information system, account management, purchasing, and admin. The system implementation result is tested using the Black-Box Testing method, which is considered the most effective to test the system comprehensively. Also, from the test result of creditworthiness determination feature is obtained accurate result with the average value of Precision = 0.876, Recall = 0.952, and F-Measure = 0.912.
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