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All Journal Jurnal Teknik Komputer AMIK BSI
Nia Nuraeni
Program Studi Teknik Informatika STMIK Nusa Mandiri Jakarta,Jl. Damai No 8 Warung Jati Barat (Margasatwa) Jakarta Selatan (Telp: 021-78839513; fax: (021) 78839421
Computer Science & IT

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Penentuan Kelayakan Kredit Dengan Algoritma Naïve Bayes Classifier: Studi Kasus Bank Mayapada Mitra Usaha Cabang PGC Nia Nuraeni
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 3, No 1 (2017): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.568 KB) | DOI: 10.31294/jtk.v3i1.1337

Abstract

In analyzing a credit sometimes a less accurate credit officer in credit analysis, so that it can lead to increased bad debts. Classification data mining algorithms are widely used to determine the credit worthiness of one Naive Bayes classifier, NBC superior in increasing the value of high accuracy but weak in the selection of attributes. After testing Naive Bayes classifier algorithm the results obtained is Naive Bayes classifier algorithm produces an accuracy of 89.33% and AUC values for 0.955.Keyword: Credit Analysis, Naive Bayes ClassifierAlgorithm 
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