RANCANG BANGUN APLIKASI PREDIKSI CALON KREDITUR PADA BANK MUAMALAT KUPANG

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Abdul G Farid
Sebastianus Adi Santoso Mola
http://orcid.org/0000-0002-1698-0758
Dony M Sihotang

Abstract

The implementation of stored-transaction data can provide a lot of useful knowledge to create
businesses intelligence in Muamalat Bank. But Muamalat Bank has not done it yet; so, it will be difficult
to give credits to the creditors. This study aimed to create business intelligence in terms of prospective
creditors prediction. It was expected that it could predict creditors in making payments using old existing
creditors forms data. The research applied the K-Nearest Neighbor algorithm (K-NN) where this
algorithm looking for similarly between render candidates and old creditors as much as k values that
still or have done their lends to Muamalat Bank Kupang. The result of this research shows that with KNN
algorithm, a creditor can be predict using data comparism. Highest accuracy can be reach when k
value=5, with accuracy level up to 80%.

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How to Cite
Farid, A., Mola, S., & Sihotang, D. (2017). RANCANG BANGUN APLIKASI PREDIKSI CALON KREDITUR PADA BANK MUAMALAT KUPANG. Jurnal Komputer Dan Informatika, 5(2), 1-5. https://doi.org/10.35508/jicon.v5i2.358
Section
Articles

References

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[4] Santosa, B. 2007. Data Mining : Teknik

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