Analisa Performa Algoritma Random Forest & Logistic Regression Dalam Sistem Credit Scoring - Billy Riantono, Bernadus; Andarsyah, Roni (Jurnal Teknologi Dan Sistem Informasi Bisnis, 2024)
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.