KOMPARASI PENERAPAN ALGORITMA C45, KNN DAN NEURAL NETWORK DALAM PROSES KELAYAKAN PENERIMAAN KREDIT KENDARAAN BERMOTOR
(1) Teknik Informatika, Fakultas Teknik, Matematika dan Ilmu Pengetahuan Alam
(*) Corresponding Author
Abstract
. In the development of business,credit problems remain tobe studie reveal edinteresting. Most problems the system imposed b ythe bank but the problem occur spreci selyt the human resources to manage credit, either on itsrelationship with the consumer or the mistake in leasing the wrong predictions in assessing consumers who apply for credit. Some computers have a lot offiel dresear chconductedto reduce the credit risk of causing harm to the company. In this study a comparison algorithm C4.5, KNN and theneural network which is appliedto the data consumer who gets the credit worthiness of motor good receptionis problematic in the install mentpaymentor not. The current methodhas not beenable to determinethe appropriatedata mining. The process of counting to three algorithms and programsadded with rapidminer can produce data that isaccurate and useful for all parties especially bess finance to further simplify the system in terms of determining the credit acceptan cevehiclesn results obtained C45 turns algorithmis more accuratein comparison witht woother algorithms.
Keywords: C4.5, KNN, neural network, RapidMiner, Data Mining
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Alpayadin, Ethem. 2010. Introduction to Machine Learning. The MIT Press. London.
Bungin, B. 2005. Metodologi Penelitian Kuantitatif. Kencana. Jakarta.
Gorunescu, Florin. 2011. Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg. Springer
Han, J.,& Kamber, M.. 2006. Data Mining Concept and Tehniques. San Fransisco. Morgan Kauffman.
Hariani, Iswi. 2010. Restrukturisasi dan Penghapusan Kredit Macet. Jakarta: PT Elexmedia Komputindo.
Jiang,Yi. et al. 2007. A Bank Customer Credit Evaluation Based on the Decision Tree and the Simulated Annealing Algorithm. Journal of Department of Computer Science Xiamen University . IEEE International Co 8-11 July 2008.
Kusrini, dan Luthfi, Emha Taufik. 2009. Algoritma Data Mining, Edisi I, Yogyakarta:Andi Publishing.
Larose, Daniel T. 2005. Discovering Knowledge in Data: An Introduction to Data mining. Jhon Willey & Son Inc., New Jersey.
Liao. 2007. Recent Advances in Data Mining of Enterprise Data: Algorithms and Application. Singapore. World Scientific Publishing
Linof, Gordon S & Berry, Michael J. 2011. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Indiana. Wiley Publishing.
Maimon, Oded & Rokach, Lior. 2010. Data Mining and Knowledge Discovey Handbook. New York: Springer
Mania, Hasan & Patel. 2011. Comparative study of Naïve Bayes Classifier and KNN for Tuberculosis. International Journal of Computer Applications (IJCA).
Mujiasih,Subekti. 2011. Pemanfaatan Data Mining Untuk Prakiraan Cuaca. Jurnal Meteorologi dan Geofisika. 12 (2):189-195.
Rivai,Veithzal.,& Veithzal, Andria Permata. 2006. Credit Management Handbook. Jakarta: Raja Grafindo Persada.
Satchidananda, S S & Jay B.Simha. 2006. Comparing Decision Trees With Logistic Regression For Credit Risk Analysis (SAS APAUGC)
Sugiyono 2001. Metode Penelitian Bisnis. CV.Alphabeta. Bandung.
Vercellis,Carlo. 2009. Business Intelegent: Data Mining and Optimization for Decision Making. Southern Gate. Chichester. west Sussex. John Willey & Sons, Ltd.
Yadav, Kumar , Surjeet & Pal, Saurabh. 2012. Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal (WCSIT) . 2 (2) 51-56.
DOI: http://dx.doi.org/10.30998/faktorexacta.v9i1.744
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