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

Abstract

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 microfinancing programs. 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 aims to 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 analysis parameters consist of income, 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 research showed 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-validation shows 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.