IMPLEMENTASI ALGORITMA DATA MINING NAIVE BAYES PADA KOPERASI
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Emerensye S. Y. Pandie(1*)
Universitas Nusa Cendana
(*) Corresponding Author
Abstract
One of the factors of failure in the field of credit business is the lack of accurate assessment of the ability of the debtor, thus resulting in errors in credit decisions that culminate in credit congestion. Data mining techniques can be used to assess customer ability based on past data. Debtor data that has been through the stages of data mining is then processed using Naive Bayes data mining algorithm. Naive Bayes is a simple probabilistic based prediction technique based on the application of bayes rules. Implementation using Weka 3.8 with a total of 3018 records yields a truth level of 94%.
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References
[2] Han, J.K., 2001. Data mining: Concept and Technique. San Fransisco: Morgan Kaufmann Publisher
[3] Kohavi, R., 1995. A Study of Cross Validation and Bootstrap for Accuracy Estimation and Model Selection, Lecture Note in Computer Science 6440, 114-124
[4] Moertini, S.V., 2003. Towards the use of C4.5 Algorithm for classifying Banking Dataset. Jurnal Integral 8 (2), 105-116
[5] Mohd, S.S., Rayner, A., 2010. Advanced Data mining and Applications 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I
[6] Oktrivianto, R., 2008. Aplikasi Data mining untuk Memprediksi Kelas Resiko Pemberian Kredit menggunakan Support Vector Machine (SVM). Tesis. Surabaya: Institut Teknologi Surabaya.
[7] Sucahyo, Y.G., 2003. Data mining: Menggali Informasi yang Terpendam. Website: http://www.ilmukomputer.com. Diakses tanggal 12 Januari 2011.
[8] Sujarwanto., 2011, 5809 Koperasi di Jawa Tengah Tidak Aktif. Harian Suara Merdeka tanggal 30 Juni 2011
[9] Sunaryanto, D., 2009. Penerapan K-Nearest Neighbour untuk Prediksi Resiko Kredit di PT. Telkom Kandatel Surabaya Timur. Tesis. Surabaya: Sekolah Tinggi Ilmu Komputer (STIKOM).
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