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Nuansa Informatika
Published by Universitas Kuningan
ISSN : 18583911     EISSN : 26145405     DOI : https://doi.org/10.25134/nuansa
Core Subject : Science,
Computer Science & IT
NUANSA INFORMATIKA is a peer-reviewed journal on Information and Technology for communication media academics, experts and practitioners of Information Technology in pouring ideas of thought in the field of Information Technology. NUANSA INFORMATIKA is a peer-reviewed journal on Information and Technology covering all branches of IT and sub-disciplines including Algorithms, system design, networks, games, IoT, Software engineering, Mobile applications, and others
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 21 Documents
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Search results for , issue "Vol 16, No 1 (2022)" : 21 Documents clear
Perbandingan Algoritma SVM, Random Forest Dan XGBoost Untuk Penentuan Persetujuan Pengajuan Kredit Mohammad Rizal Givari; Mochammad Riszky Sulaeman; Yuyun Umaidah
NUANSA INFORMATIKA Vol 16, No 1 (2022)
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (325.813 KB) | DOI: 10.25134/nuansa.v16i1.5406

Abstract

Credit is an option for seeking funding for most economic activities. The demand for credit is currently growing very rapidly, in line with the increasing financial needs of the community, especially in developing countries such as Indonesia. Credit analysis needs to be carried out to achieve proper and safe lending. Credit analysis is an observation to see the feasibility of a credit problem. From this analysis, the creditworthiness of the recipient will be known. This study uses the CRISP-DM methodology which consists of 6 stages, namely Bussines Understanding, Data Understanding, Data preparation, Modeling Evaluation, and Deployment by applying the classification method by comparing the SVM, Random Forest, and XGBoost algorithms. This research uses an open source dataset obtained from Kaggle. The results of the research using the SVM, random forest, and XGBoost algorithms get the highest accuracy, recall, precision values in the XGBoost model with 82% accuracy, 70% recall, and 92% precision.

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