JOURNAL

Layanan journal yang disediakan oleh Perpustakaan Universitas Gunadarma

Enhancing Decision Tree Performance in Credit Risk Classification and Prediction

Judul Artikel:Enhancing Decision Tree Performance in Credit Risk Classification and Prediction
Judul Terbitan:ULTIMATICS JURNAL TEKNIK INFORMATIKA
ISSN:2085-4552
Bahasa:IND
Tempat Terbit:TANGERANG
Tahun:0000
Volume:Vol. 7 Issue 1 0000
Penerbit:Universitas Multimedia Nusantara (UMN)
Frekuensi Penerbitan:1 x 1 tahun
Penulis:Raymond Sunardi Oetama
Abstraksi: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.
Kata Kunci:Data Mining; Decision Tree; Resampling; Credit Analyst.
Lokasi:P50
Terakreditasi:belum