ANALISIS CREDIT SCORING TERHADAP STATUS PEMBAYARAN BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN BOOTSTRAP AGGREGATING K-NEAREST NEIGHBOR

  • Putri Sri Astuti Universitas Mulawarman
  • Memi Nor Hayati
  • Rito Goejantoro
DOI: https://doi.org/10.30598/barekengvol15iss4pp735-744
Keywords: classification, k-nearest neighbor, bootstrap aggregating, credit

Abstract

Classification is the process of grouping objects that have the same characteristics into several categories. This study applies a combination of classification algorithms, namely Bootstrap Aggregating K-Nearest Neighbor in credit scoring analysis. The aim is to classify the credit payment status of electronic goods and furniture at PT KB Finansia Multi Finance in 2020 and determine the level of accuracy produced. Credit payment status is grouped into 2 categories, namely smoothly and not smoothly. There are 7 independent variables that are used to describe the characteristics of the debtor, namely age, number of dependents, length of stay, years of service, income, amount of payment, and payment period. The application of the classification algorithm at the credit scoring analysis is expected to assist creditors in making decisions to accept or reject credit applications from prospective debtors. The results showed that the accuracy obtained from the Bootstrap Aggregating K-Nearest Neighbor algorithm with a proportion of 90:10, m=80%, C=73, and K=5 was the best, which was 92.308%.

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Published
2021-12-01
How to Cite
[1]
P. Astuti, M. Hayati, and R. Goejantoro, “ANALISIS CREDIT SCORING TERHADAP STATUS PEMBAYARAN BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN BOOTSTRAP AGGREGATING K-NEAREST NEIGHBOR”, BAREKENG: J. Math. & App., vol. 15, no. 4, pp. 735-744, Dec. 2021.
Issue
Vol 15 No 4 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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