Main Article Content
Abstract
Sewa guna usaha (leasing) atau sering disingkat SGU adalah kegiatan pembiayaan dalam bentuk penyediaan barang modal baik secara sewa guna usaha dengan hak opsi (finance lease) maupun sewa guna usaha (lessee) selama jangka waktu tertentu berdasarkan pembayaran secara angsuran, kegiatan ini bisa dikatakan sebagai kegiatan kredit, kredit adalah penyediaan uang atau tagihan yang dapat dipersamakan dengan itu, berdasarkan persetujuan atau kesepakatan pinjam meminjam antara lembaga keuangan dengan pihak lain yang mewajibkan pihak peminjam melunasi utangnya setelah jangka waktu tertentu dengan pemberian bunga.Pengajuan aplikasi kredit oleh calon pelanggan sekarang sangatlah mudah, hal ini dikarenakan pengajuan kredit bisa dilakukan oleh semua orang sepanjang memenuhi syarat tertentu. Persaingan perusahaan penyedia kredit menjadi sangat pesat dan prediksi konsumen kredit adalah hal yang sangat penting. Dari permasalahan ini diperlukan suatu model yang mampu mengklasifikasikan sekaligus memprediksi pelanggan mana saja yang bermasalah dan tidak bermasalah. Model Naïve Bayes berbasis Particle Swarm Optimization (PSO) ternyata mampu meningkatkan akurasi dalam menganalisa kelayakan kredit, semakin besar data set yang digunakan maka akurasi model Naïve Bayes berbasis Particle Swarm Optimization (PSO) akan semakin meningkat, akurasi yang didapat oleh model ini untuk data set yang digunakan adalah: Agiing 2010 (96,75%), Agiing 2011 (97,95%), Japan credit approval (84,77%) dan Australia credit approval (87,83%).
Kata Kunci: Analisa kredit, penilaian kredit, Naïve Bayes berbasis PSO.
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References
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- J. Lin, “Weighted Naive Bayes Classification Algorithm Based on Particle Swarm Optimization,” IEEE, pp. 444-447, 2011.
- J. Zurada, “Could Decision Trees Improve the Classification Accuracy and Interpretability of Loan Granting Decisions ?,” International Conference on System Sciences, pp. 1-9, 2010.
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References
“Peraturan Presiden No 9,” 2009.
“Undang-Undang Republik Indonesia No 10,” 1998.
Y. Ling, “Application of the PSO-SVM model for Credit Scoring,” Seventh International Conference on Computational Intelligence and Security, 2011.
Y. Ma, “Research of SVM Applying in the Risk of Bank ’ s Loan to Enterprises,” America, no. 3, pp. 1-5, 2010.
D. Zhang, H. Hu, and H. Zhang, “Risk Analysis of Credit Rating Business for Commercial Banks on Small and Medium-sized Enterprise,” in 2011 International Conference on Information Management, Innovation Management and Industrial Engineering, 2011, pp. 312-315.
F. C. Li, F. L. Chen, and G. E. Wang, “Comparison of Feature Selection Approaches based on the SVM Classification 12,” Industrial Engineering, pp. 400-404, 2008.
A. Heiat, “Modeling Consumer Credit Scoring Through Bayes Network Heiat,” Social Sciences, vol. 1, no. 3, pp. 132-141, 2011.
T. W. Liao, Recent Advanced in Data Mining of Enterprise Data: Algorithm and Applications, Series on . New Jersey: World Scientific Publishing Co.Pte.Ltd, 2007.
W. Shuang-cheng, “Conditional Markov Network Hybrid Classifiers Using on Client Credit Scoring,” International Symposium on Computer Science and Computational Technology, 2008.
Y. Jiang and L. H. Wu, “Credit Scoring Model Based on Simple Naive Bayesian Classifier and a Rough Set,” IEEE, pp. 1-4, 2009.
A. Keramati and N. Yousefi, “A Proposed Classification of Data Mining Techniques in Credit Scoring,” International Conference on Industrial Engineering and Operations Management, pp. 416-424, 2011.
W. Feng, “Application of SVM Based on Principal Component Analysis to Credit Risk Assessment in Commercial Banks,” Global Congress on Intelligent System, pp. 49-52, 2009.
F.-chia Li, P.-kai Wang, and G.-en Wang, “Comparison of the Primitive Classifiers with Extreme Learning Machine in Credit Scoring,” Proceedings of the IEEE IEEM, vol. 2, no. 4, pp. 685-688, 2009.
X. Y. Yu and A. Wang, “Genetic Algorithm Based Bayesian Network for Customers ’ Behavior Analysis,” Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 406-409, 2010.
M. J. Islam, Q. M. J. Wu, M. Ahmadi, and M. A. Sid-ahmed, “Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers,” International Conference on COnvergence Information Technology, pp. 1541-1546, 2007.
F. Gorunesco, Data Mining Concept, Model and Techniques. Romania: Scientific Publishing Services Pvt.Ltd, 2011.
J. Lu and C. X. Ling, “Comparing Naive Bayes , Decision Trees , and SVM with AUC and Accuracy,” Proceeding of the Third IEEE International Conference on Data Mining, pp. 3-6, 2003.
J. Lin, “Weighted Naive Bayes Classification Algorithm Based on Particle Swarm Optimization,” IEEE, pp. 444-447, 2011.
J. Zurada, “Could Decision Trees Improve the Classification Accuracy and Interpretability of Loan Granting Decisions ?,” International Conference on System Sciences, pp. 1-9, 2010.
B. Santosa, “Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis,” 2007.
P. Machine and L. Tools, No Title. .
D. T. Larose, Discovering Knowledge In Data An Introduction to Data Mining. Canada: John Wiley & Sons, Inc., Hoboken, 2005.