RANCANG BANGUN APLIKASI PREDIKSI CALON KREDITUR PADA BANK MUAMALAT KUPANG

  • Abdul G Farid(1)
    Universitas Nusa Cendana
  • Sebastianus Adi Santoso Mola(2*)
    Universitas Nusa Cendana http://orcid.org/0000-0002-1698-0758
  • Dony M Sihotang(3)
    Universitas Nusa Cendana
  • (*) Corresponding Author
Keywords: creditors candidates, creditors, prediction, K-Nearest Neighbor

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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References

[1] Farid, Abdul. 2014, Penerapan Algoritma K-Nearest Neighbor Untuk Prediksi Calon
Kreditur (Studi Kasus Bank Muamalat Kupang). Skripsi Ilmu Komputer. Kupang:
Universitas Nusa Cendana.
[2] Jogiyanto, H. 2005 . Analisis dan Desain Sistem Informasi, Andi Publiser, Yogyakarta.
[3] Putranta, Dewa Hastha. 2004, Pengantar Sistem Dan Teknologi Informasi. Amus,
Yogyakarta.Waluya, Harry. Sistem Informasi Komputer Dalam Bisnis. Rineka Cipta,
Jakarta,1997.
[4] Santosa, B. 2007. Data Mining : Teknik

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Published
2017-10-31
How to Cite
[1]
A. Farid, S. Mola, and D. Sihotang, “RANCANG BANGUN APLIKASI PREDIKSI CALON KREDITUR PADA BANK MUAMALAT KUPANG”, jicon, vol. 5, no. 2, pp. 1-5, Oct. 2017.
Section
Articles

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