Classification Algorithm Implementation Of Data Mining In The Determination Of Giving Loan Cooperative

Classification Algorithm Implementation Of Data Mining In The Determination Of Giving Loan Cooperative

• By Suryanto,;
• Seminar Internasional
• 128
• 160-167

Cooperatives as a form of organization that is important in promoting economic growth . Credit unions be an alternative for people to get funding in an effort to improve their quality of life , fulfillment of daily needs and develop usaha.Tidak doubt , provide loan funds to customers will surely emerge problems , such as late customer pays the mortgage funds , abuse funds for other purposes , the client fails to expand its business so as to result in cooperative funds do not flow or it can lead to credit macet.Tujuan this research is to establish a model of an algorithm that can predict the behavior of troubled borrowers . Data Mining is one of the methods that can be used to analyze existing data chunks that can be used to summarize the data provide specific information related to the data . Data Mining classification of the decision tree using the C4.5 algorithm in the form of a rule statement. Decision tree model was able to improve the accuracy in analyzing the credit worthiness of potential borrowers filed . The richer the information or knowledge that is contained by the training data , the accuracy of the decision tree will increase . The results obtained for the value of 86.13 % Accuracy , Precision Value 89.94 % , 92.61 % recall rate curve AUC is worth 0842 each prediction entry into the category of good value and excellent classification classification

• Katakunci behavior of borrowers , Data Mining , C4.5 Algorithm
• URL http://lppm.bsi.ac.id/proceeding-seminar-international/proceeding-issit-2014
• Penerbit LPPM BSI Jakarta, Jakarta DKI Jakarta

REFERENSI

[1] Alpaydin, Ethem. (2010). Introduction to M achine Learning. London: The MIT Press
[2] Anwar, Syaiful (2012) Penerapan data mining untuk memprediksi perilaku nasabah kredit: studi kasus BPR Marcorindo perdana ciputat Tesis,Magister Ilmu Komputer,STMIK Nusa Mandiri,Jakarta.
[3] Bramer,Mark(2007) Principles of Data Mining, Verlag London Limited Springer
[4] C.R.Kothari. (2004). Research Methology Methods and Techniques. India: New Age International Limited.
[5] Firmansyah (2011) Penerapan Algoritma Klasifikasi C4.5 untuk penentuan Kelayakan Pemberian Kredit Koperasi,Tesis,Magister Ilmu Komputer, STMIK Nusa Mandiri, Jakarta.
[6] Gorunescu, Florin (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer

[7] Han, J., & Kamber, M. (2006). Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman.
[8] Jiang, Y. (2009 ). Credit Scoring Model Based on Decision Tree and the Simulated Annealing Algorithm. 2009 World Congress on Computer Science and Information Engineering (hal. 18 - 22). Los Angeles: IEEE Computer Society.
[9] Kasmir. (2011). Analisis Laporan Keuangan. Jakarta : PT. Rajagrafindo Persada
[10] Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Yogyakarta: Andi Publishing.
[11] Kotsiantis, S., Kanellopoulos, D., Karioti, V., & Tampakas, V. (2009). An ontology-based portal for credit risk analysis. 2009 2nd IEEE International Conference on Computer Science and Information Technology, (hal. 165169). Beijing.
[12] Leidiyana, Heny (2011) Komparasi Algoritma Klasifikasi Data Mining Dalam Penentuan Resiko Kredit Kepemilikan Kendaraan Bemotor ,Tesis,Magister Ilmu Komputer,STMIK Nusa Mandiri,Jakarta.
[13] Lai, K. K., Yu, L., Zhou, L., & Wang, S. (2006). Credit Risk Evaluation with Least Square Support Vector Machine. Springer-Verlag , 490-495
[14] Larose, D. T. (2005).Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc.
[15] Liao. (2007). Recent Advances in Data Mining of Enterprise Data: Algorithms and Application. Singapore: World Scientific Publishing
[16] Maimon, Oded&Rokach, Lior.(2005). Data Mining and Knowledge Discovey Handbook. New York: Springer
[17] Odeh, O. O., Featherstone, A. M., & Das, S. (2010). Predicting Credit Default: Comparative Results from an Artificial Neural Network, Logistic Regression and Adaptive Neuro-Fuzzy Inference System. EuroJournals Publishing, Inc. 2010 , 7-17.
[18] Profil Koperasi Serba Usaha “Ceger Jaya” Kelurahan Ceger-Cipayung Jakarta Timur
[19] Sumathi, & S., Sivanandam, S.N. (2006). Introduction to Data Mining and its Applications. Berlin Heidelberg New York: Springer
[20] Sholichah, Alfiyatus,(2009),Data Mining Untuk Pembiayaan Murabahah Menggunakan Association Rule (Studi Kasus BMT MMU Sidogiri),Skripsi,Universitas Islam Negeri Maulana Malik Ibrahim,Malang.
[21] Sogala,(2006) Satchidananda S. Comparing the Efficacy of the Decision Trees with Logistic Regression for Credit Risk Analysis. India
[22] Vercellis, Carlo (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate, Chichester, West Sussex: John Willey & Sons, Ltd.
[23] Zemke , Stefan (2003) Data Mining for Prediction.Financial Series Case, Doctoral Thesis The Royal Institute of Technology,Sweden Department of Computer and Systems Sciences
[24] Zurada, J. (2010). Could Decision Trees Imnprove the Classification Accuracy and Interpretability of Loan Granting Decisions. HICSS '10 Proceedings of the 2010 43rdHawaii International Conference on System Sciences, (hal. 19). Koloa.
Suryanto, is a lecturer of Computer Science, AMIK BSI. He received a Master Degree in Computer Science from STMIK Nusa Mandiri Program of Information System in 2010 on Program of “Management Information System”. M. Kom research interests are in Management Information system. he is a researcher who won the novice faculty research grants DIKTI period 2013-2014
Nandang Iriadi, is a lecturer of Computer Science, of Computer Science, AMIK BSI He received a Master Degree in Computer Science from STMIK Nusa Mandiri in 2010 on “Management Information system”. M. Kom research interests are in Management Information System. he is a researcher who won the novice faculty research grants DIKTI period 2013-2014