Abstract

Credit is one of the common practices that provide benefits for financial or non-financial institutions. However on the other hand, aid loans also have higher risks if the institutions give the wrong decision in giving a loan. Credit Scoring is one of techniques that can determine whether it is feasible to given a loan or not. The selection of a credit scoring model greatly determines the value in classifying credit that is feasible or not to giving a loan. Decision Support System (DSS) is one system that can be used to overcome this problem. The advantages of DSS are being able to overcome the problems that have semi-structured and unstructured data. In this study, DSS was supported by using Artificial Neural Network Backpropagation method and TOPSIS method to find the priority for seeking eligibility. Accuracy results obtained in this study reached 98,69% with the number of iteration is 300, the number of training data is 30, neuron hidden 12 and error tolerance is 0.001. TOPSIS method succeeded in ranking 185 data selected as recipients of credit.
 Keywords:Credit Scoring, Decision Support System (DSS), Artificial Neural Network (ANN), Backpropagation, TOPSIS.

Highlights

  • Credit is one of the common practices that provide benefits for financial or non-financial institutions

  • While For (Epoc || Nilai Error each Layer dalam Jaringan maxError) menghasilkan perubahan bobot dari pengaturan For each Neuron layer (Z)

  • A Neural Network Approach for Credit Risk Evaluation, data latih lebih banyak maka kemungkinan untuk vol 48, no. 4, pp. 733-755

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Summary

Metode Penelitian

Penelitian ini menggunakan metode jaringan saraf tiruan backpropagation dan TOPSIS untuk menentukan credit scoring kelayakan calon debitur. Metode backpropagation akan digunakan sebagai metode pembelajaran, sedangkan metode TOPSIS digunakan sebagai metode perangkingan untuk menentukan kualitas keputusan. 2 Input: atribut sisa akan digunakan bersamaan dengan Vektor Input i perangkingan dalam metode TOPSIS. Setelah data latih sudah siap, selanjutnya dilakukan Inisialisasi jumlah neuron input, hidden, dan pengaturan jaringan telebih dahulu dengan menetapkan output. Inisialisasi semua antara -1 & 1 bobot dengan nilai random, neuron hidden dan learning rate sebagai bentuk Inisialisasi Epoc n percobaan. Kemudian dilakukan pelatihan terhadap Inisialisasi maxError m data, yang mana hasil dari pelatihan ini akan. While For (Epoc || Nilai Error each Layer dalam Jaringan maxError) menghasilkan perubahan bobot dari pengaturan For each Neuron layer (Z). 1. Kalkulasi jumlah bobot dan bias setiap input yang menuju hidden Neuron

Update setiap bobot dalam jaringan
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