UTILIZING SCIENCE DATA TO INCREASING THE NUMBER MSME DEBTORS AT PT.BANK CENTRAL ASIA.TBK (CASE STUDY OF PT. BANK CENTRAL ASIA.TBK KCU TEBING TINGGI)
DOI:
https://doi.org/10.54443/jaruda.v2i1.75Keywords:
Data Science, Machine Learning, Random Forest, MSME Debtors, BCA.Abstract
This study aims to increase the number of MSME debtors at the BCA Tebing Tinggi Branch. Since the enactment of Bank Indonesia Regulation Number 23/13/PBI/2021 concerning the Macroprudential Inclusive Financing Ratio (RPIM) for Conventional Commercial Banks, Sharia Commercial Banks, and Sharia Business Units. So Commercial Banks began to adjust the percentage of the use of funds that would be used to finance MSMEs and PBR. BCA Tebing Tinggi Branch is committed to meeting the increase in the percentage of RPIM. One way that can be used to explore Potential Funding is by Utilizing Data Science. Data science studies data, especially quantitative data, with the aim of finding hidden patterns in the data. Researchers will study profile information and transaction patterns in accounts to find MSME customers who are given the right financing. This study processes data using the Machine Learning method with the Random Forest algorithm.
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