Penggunaan Algoritma C4.5 untuk Rekomendasi Peminjaman Uang pada Koperasi Sejahtera Mandiri

Tegar, Muhammad Imam; Haji, Wachyu Hari

doi:10.24089/j.sisfo.2019.09.002

Cited by 1 publication

(1 citation statement)

References 9 publications

(16 reference statements)

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“…Previous research in the analysis of lending status shows that the analytical model built using the C4.5 algorithm shows good results in the classification of lending status [12]. Furthermore, the same research by applying the Simple Additive Weighting method produces alternative solutions in making decisions regarding the feasibility of providing loans [13].…”

Section: Introductionmentioning

confidence: 99%

Optimization Analysis Model Determining PNMP Mandiri Loan Status Based on Pearson Correlation

Putra

Widya

Afira

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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PNPM Mandiri is an organization engaged in financing small and medium enterprises in the community. The problem that always occurs is an error in determining the loan status resulting in bad credit. This study aims to present a classification analysis model for determining loan status at PNPM Mandiri. The classification analysis model was built using the Perceptron algorithm artificial neural network. The analysis model will later be optimized using the Person Correlation (PC) method to measure the accuracy of the variables used. The research dataset is based on historical data from the last 2 years as many as 67 data samples. The analysis variables consist of Business Type (X1), Loan Amount (X2), Collateral (X3), Income (X4), and Expenses (X5). The results of the analysis show that the model built can provide optimal classification results. These results can be seen based on the results of variable measurements using the PC method indicating that variable X2 has no significant relationship. With the results of these measurements, the performance of the artificial neural network presents maximum results in determining loan status. Overall, the results of this study can provide an effective analytical model as well as an alternative solution for determining loan status.

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Section: Introductionmentioning

confidence: 99%