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Enhancing Decision Tree Performance in Credit Risk Classification and Prediction

Oetama, Raymond Sunardi (2015) Enhancing Decision Tree Performance in Credit Risk Classification and Prediction. Ultimatics : Jurnal Teknik Informatika, 7 (1). ISSN 2085-4552

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Official URL: https://ejournals.umn.ac.id/index.php/TI/article/v...

Abstract

This study is focused on enhancing Decision Tree on its capabilities in classification as well as prediction. The capability of decision tree algorithm in classification outperforms its capability in prediction. The classification quality will be enhanced when it works with resampling techniques such as Adaboost.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming
600 Technology (Applied Sciences) > 600 Technology > 600 Technology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Teknik Informatika > Program Studi Sistem Informasi
Depositing User: mr admin umn
Date Deposited: 25 Nov 2021 08:40
Last Modified: 25 Nov 2021 08:40
URI: http://kc.umn.ac.id/id/eprint/19289

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