PENERAPAN CLASSIFICATION BASE ON MULTIPLE ASSOCIANTON RULE PADA ANALISA RESIKO KREDIT USAHA MIKRO DI BANK SYARIAH MANDIRI KCP PRAYA

Moh. Farid Wajdi, Hamzan Ahmadi, L. M. Samsu

Abstract


Based on data of customer Micro Credit in BSM KCP Praya, there is a fact customer with troubled credit status is high enough percentage level. Of the total 171 customers, found as many as 42 clients with problematic status or by 24.56%. This is despite the presentation is still below the current credit status, it still would be a problem at KCP Praya because it would result in the loss and affect the balance of assets or general praya KCP. For the analysis of micro credit in KCP Praya has not used methods or techniques specific, but in another study that examines in particular the problem of credit ratings many use classification algorithm C4.5, SVM, neural network, logistic regression and classification methods traditional or other common. Particularly in this study, the authors use the technique Classification Asociative approach CMAR, because it is known proven Accuracy better than traditional methods. Results of measurement accuracy for 171 datasets micro credit customers in BSM KCP Praya obtained value reached 99.42% accuracy. Credit risk analysis that will be examined starting from the identification of variables that influence the existing loan in the training data, generate a model Classification Association Rule (CARs) with CMAR method, do ranking and pruning rule to get the best classifier models, and carry out testing of data to predict credit risk. At last evaluate performance and accuracy of the results in the form of credit risk prediction

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