Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Kevin Benedictus Simarmata
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
Analisa Rekomendasi Fitur Persetujuan Pinjaman Perusahaan Financial Technology Menggunakan Metode Random Forest Kevin Benedictus Simarmata; Kristoko Dwi Hartomo
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.2258

Abstract

Financial technology is an industry that utilizes technology to support the financial system and the delivery of financial services more effectively and efficiently.One type of financial technology is peer-to-peer lending. To get a peer-to-peer loan, the company should collect data such as annual income, credit history, work history, and others, the companies perform screening on applications made by borrowers. The results of screening are applications that are accepted and rejected with should be done rapidly and accurately. The machine learning approach is suitable to overcome this problem and can predict the factors that influence loan approval using the feature importance. This study wants to predict the factors that influence loan approval using the Lending Club dataset. The stages of the research method used include data understanding, feature extraction, data pre-processing, exploratory data analysis (EDA), modeling, and insight. The modeling process uses the random forest algorithm because it runs efficiently on large amounts of data. The evaluation model used in the modeling process is recall with quite high result, namely 0.97. Insight obtained from all stages, there are five major determining factors, namely annual income, monthly payments of the borrower, interest rates, investor funds, and the length of work.
Co-Authors Kristoko Dwi Hartomo