Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Vol 3 No 1 (2019): April 2019

Credit Scoring Kelayakan Debitur Menggunakan Metode Hybrid ANN Backpropagation dan TOPSIS

Susan Dwi Saputri (Universitas Sriwijaya)
Ermatita Ermatita (Universitas Sriwijaya)



Article Info

Publish Date
13 Apr 2019

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.

Copyrights © 2019






Journal Info

Abbrev

RESTI

Publisher

Subject

Computer Science & IT Engineering

Description

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat ...