Klasifikasi Kelayakan Peminjaman Uang Untuk Nasabah (Studi Kasus: Koperasi Kredit Karya Kasih Palembang)
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Institusion
Universitas Katolik Musi Charitas
Author
Lbn Gaol, Ida Warti
Subject
T Technology (General)
Datestamp
2018-11-15 07:24:43
Abstract :
Koperasi kredit karya kasih palembang always do savings and loan activities with customers. In this activity there are several processes that must be done by the cooperative, prospective customers fill out the form, and direct interviews with the cooperative, and so forth. Interview results often occur inaccuracy data because it is still influenced by subjectivity factor and takes a long time to get results so built a system that can help the cooperative in classifying the nominal customer loans. One commonly used classification method is the Naive Bayes Classifier. The Naive Bayes Classifier performs calculations based on the criteria used in classifying the tuition. This study uses Algorithm Naive Bayes Classifier with some predefined criteria, namely Status House, Work Status, Total savings, and income. The results of this study are, the program created can classify the nominal loans into four predetermined classes of K1 (<2000,000), K2 (2,000,000 - 5,000,000), K3 (6,000,000 - 15,000,000) and K4 (> 15,000,000). The application made able to classify the nominal loan money for the customer at koperasi kredit karya kasihPalembang.
Koperasi kredit karya kasih palembang always do savings and loan activities with customers. In this activity there are several processes that must be done by the cooperative, prospective customers fill out the form, and direct interviews with the cooperative, and so forth. Interview results often occur inaccuracy data because it is still influenced by subjectivity factor and takes a long time to get results so built a system that can help the cooperative in classifying the nominal customer loans. One commonly used classification method is the Naive Bayes Classifier. The Naive Bayes Classifier performs calculations based on the criteria used in classifying the tuition. This study uses Algorithm Naive Bayes Classifier with some predefined criteria, namely Status House, Work Status, Total savings, and income. The results of this study are, the program created can classify the nominal loans into four predetermined classes of K1 (<2000,000), K2 (2,000,000 - 5,000,000), K3 (6,000,000 - 15,000,000) and K4 (> 15,000,000). The application made able to classify the nominal loan money for the customer at koperasi kredit karya kasihPalembang.
File :
IF-2018-1313009-cover.pdf
IF-2018-1313009-abstract.pdf
IF-2018-1313009-complete.pdf
IF-2018-1313009-summary_id.pdf
IF-2018-1313009-tableofcontent.pdf
IF-2018-1313009-chapter1.pdf
IF-2018-1313009-chapter2.pdf
IF-2018-1313009-chapter3.pdf
IF-2018-1313009-chapter4.pdf
IF-2018-1313009-conclusion.pdf
IF-2018-1313009-reference.pdf
IF-2018-1313009-attachment.pdf
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