Analisis Statistik Pembayaran Kredit Menggunakan Metode Random Forrest Classfier Dan LightGBM

Akni Widiyastuti; Chairani Chairani; Kurniawati Kurniawati; Siti Aminah; Muhamad Abror

doi:10.56327/jtksi.v6i3.1551

Analisis Statistik Pembayaran Kredit Menggunakan Metode Random Forrest Classfier Dan LightGBM

Akni Widiyastuti⁽¹⁾, Chairani Chairani⁽²⁾, Kurniawati Kurniawati⁽³⁾, Siti Aminah⁽⁴⁾, Muhamad Abror⁽⁵⁾,

(1) Fakultas Ilmu Komputer, Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia
(2) Fakultas Ilmu Komputer, Informatics & Business Institute Darmajaya, Lampung
(3) Fakultas Ilmu Komputer, Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia
(4) Fakultas Ilmu Komputer, Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia
(5) Fakultas Ilmu Komputer, Informatics & Business Institute Darmajaya, Lampun

Corresponding Author

Abstract

Banking also takes part in marketing technology based on financial services such as the following: Banking, Credit and Loans, Financial Advisory, and Consumer Convenience. By automating most of the banking processes and adding speed, transparency, and security to them, Fintech applications also indirectly contribute to a seamless experience for customers. It is known that one of the banks in Indonesia has also implemented a fintech system, one of which is in terms of processing credit payments (online credit). Some people have applied for credit at the bank by making payments digitally or online. Banks have collected data from their customers who have made credit online. These data are collected to obtain information that helps banks see the status of credit payments. In the research results, the accuracy value was obtained by using the Random Forrest Classifier and LightGBM method comparisons, the values obtained from the AUC for the validation set were around 65% and 80%.

Keywords

Credit, Random Florest Classfier, LightGBM

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