Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis
Billy Riantono, Bernadus
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisa Performa Algoritma Random Forest & Logistic Regression Dalam Sistem Credit Scoring Billy Riantono, Bernadus; Andarsyah, Roni
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 2 (2024): April 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i2.1308

Abstract

The rapid advancement of technology, particularly in the field of Artificial Intelligence (AI), has had a significant impact across various industries. One increasingly popular implementation is ChatGPT, enabling more intuitive human-computer interactions. Moreover, AI has transformed the landscape of the financial sector, particularly in Credit Scoring. Using Supervised Machine Learning, algorithms like Random Forest and Logistic Regression are employed to enhance accuracy and efficiency in the Credit Scoring process. However, comparing the accuracy between these two algorithms remains a question. Therefore, this research aims to compare the accuracy levels of Random Forest and Logistic Regression in the context of Credit Scoring. From the research that have been conducted got result Random Forest given better AUC score on 0.90 than Logistic Regression which only got 0.89.
Co-Authors Andarsyah, Roni