JISA (Jurnal Informatika dan Sains)
Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)

South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts

Yoga Religia (Universitas Pelita Bangsa)
Gatot Tri Pranoto (Universitas Trilogi)
Egar Dika Santosa (Universitas Dian Nuswantoro)

Article Info

Publish Date
27 Dec 2020


Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.

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Journal Info





Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering


JISA (Jurnal Informatika dan Sains) is an electronic publication media which publishes research articles in the field of Informatics and Sciences, which encompasses software engineering, Multimedia, Networking, and soft computing. Journal published by Program Studi Teknik Informatika Universitas ...