Hybrid Data Mining For Member Determination And Financing Prediction In Syariah Financing Saving And Loan Cooperatives

Hybrid Data Mining For Member Determination And Financing Prediction In Syariah Financing Saving And Loan Cooperatives

KSPPS;hybrid data mining;, k-medoids;naïve bayes;k-nearest neighbor (k-NN)

Syariah Financing Saving And Loan Cooperatives (KSPPS) is an Islamic financial institution aimed at people who are on the lower middle scale to lift the economy of small communities through microfinancingprograms. Problems that often occur in member recommendations to get KSPPS financing are often not on target. In addition, The amount of member financing is often problematic due to a lack of analysis, resulting in poor financing instalments. This research aimsto present an analysis model for clustering and classification using hybrid data mining algorithms.This research method is using hybrid data mining Algorithms, namely K-Medoids, Naïve Bayes, and k-Nearest Neighbors (k-NN). This study uses the historical dataset of the last two years on KSPPS BMT Dadok Tunggul Hitam as a total of 70 data samples. The analysisparameters consist ofincome, business, residence Status, financing application, billing history, and balance amount. The best analysis Model will be obtained by comparing the results between Naïve Bayes with K-Medoids, and K-Nearest Neighbor (k-NN) with K-Medoids. The results of this researchshowed the best performance is using the hybrid Naïve Bayes data mining model with K-Medoids which has an accuracy of 90.91% for data split 70:30, while performance with K-fold cross-validationshows an accuracy of 93.49%using this algorithm. Overall, the results of this study can provide an effective analysis model to determine the status of the loan.

Ondra Eka Putra1, Randy Permana2

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5683/930

Department InformationSystem, Faculty of Computer Science, Universitas Putra Indonesia YPTK Padang

28-04-2024

FAJAR BAGUS W

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