DATA MINING GROUPING THE FEASIBILITY OF APPLYING FOR CREDIT TO CUSTOMERS USING THE K-MEANS ALGORITHM METHOD ON CV. MOTORBIKE CHOICE

Authors

  • Ruth Natalia Sianturi STMIK Kaputama, Binjai, Sumatera Utara
  • Marto Sihombing STMIK Kaputama, Binjai, Sumatera Utara
  • Anton Sihombing STMIK Kaputama, Binjai, Sumatera Utara

Keywords:

Data_Mining, Filing_Credit, Algorithm_K-Means.

Abstract

In the midst of the current era of technological development, many changes have occurred in the field of transportation. Transportation is an important aspect of human life because transportation contributes to daily human life and activities. With the existence of transportation, people can easily reach somewhere far or near. Motorcycles are a means of transportation that is quite popular in the community. CV. Choice of Motors is an individual company engaged in the provision of new or used motorcycle loans. Motorcycle credit is a loan facility that aims to finance the purchase of a motorbike where the source of credit payment comes from income. Customers who apply for credit both individually and as an institution with a maximum financing of up to a certain amount along with the BPKB guarantee for the motorbike purchased. In setting credit policies, companies must first formulate credit standards and credit terms, the data needed as credit requirements include: KTP, income, employment, family cards, and other administrative requirements. Then a field survey will be carried out and then the results of the survey are analysis, after which the results of the analysis are returned to the company. Based on the results of the study, there were 4 groups of 20 data, namely group 1 with 5 data, group 2 with 8 data, and group 3 with 7 data.

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Additional Files

Published

2023-11-23

How to Cite

Natalia Sianturi, R., Sihombing, M. ., & Sihombing, A. . (2023). DATA MINING GROUPING THE FEASIBILITY OF APPLYING FOR CREDIT TO CUSTOMERS USING THE K-MEANS ALGORITHM METHOD ON CV. MOTORBIKE CHOICE. Journal of Mathematics and Technology (MATECH), 2(2), 49–60. Retrieved from http://journal.binainternusa.org/index.php/matech/article/view/145

Issue

Vol. 2 No. 2 (2023): Journal MATECH (November 2023)

Section

Articles

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Copyright (c) 2023 Ruth Natalia Sianturi, Marto Sihombing, Anton Sihombing

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