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Jeprianto Sinaga
STMIK Pelita Nusantara
Computer Science & IT Education

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Data Mining Classification Of Filing Credit Customers Without Collateral With K-Nearest Neighbor Algorithm (Case study: PT. BPR Diori Double) Jeprianto Sinaga; Bosker Sinaga
Journal of Computer Networks, Architecture and High Performance Computing Vol. 2 No. 2 (2020): Computer Networks, Architecture and High Performance Computing
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnapc.v2i2.401

Abstract

Unsecured loans are the community's choice for lending to banks that provide Reviews These services. PT. RB Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that Affect the customer's classification so that credit can be approved, the which is then submitted to the commissioner for credit approval. But what if Reviews those who apply for credit on the same day in large amounts, of course this will the make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to Facilitate the work of the assessor team in the process of analyzing the attributes that Affect customer classification. To find out the classification of customers who apply for unsecured loans for using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral.
Co-Authors Bosker Sinaga