Anindya Ayu Prahartiwi
Institut Informatika Indonesia Surabaya

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Algoritma C4.5 Untuk Klasifikasi Kelayakan Kredit Calon Debitur Pada Sistem Informasi Penjualan Raymond Sutjiadi; Titasari Rahmawati; Anindya Ayu Prahartiwi
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 12, No 2 (2022): Volume 12 Nomor 2 Tahun 2022
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol12iss2pp89-97

Abstract

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.