PREDIKSI TINGKAT RISIKO KREDIT DENGAN RANDOM OVER-UNDER SAMPLING PADA METODE ENSEMBLE MENGGUNAKAN ALGORITMA DECISION TREE ID3, RANDOM FOREST DAN REGRESI LOGISTIK BINER

ANGGRAINI, FAHIRA and Resti, Yulia and Cahyono, Endro Setyo (2022) PREDIKSI TINGKAT RISIKO KREDIT DENGAN RANDOM OVER-UNDER SAMPLING PADA METODE ENSEMBLE MENGGUNAKAN ALGORITMA DECISION TREE ID3, RANDOM FOREST DAN REGRESI LOGISTIK BINER. Undergraduate thesis, Sriwijaya University.

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Abstract

Credit-granting activities are included in business activities that have a high risk and affect the sustainability of the company as well as other financial institutions. In credit activities, non-performing loans often occur due to the failure to repay a number of loans in accordance with the agreed time. The problem of providing credit can be overcome, one of which is by identifying and predicting prospective customers before giving credit. Datasets used to predict sometimes have class imbalance problems. This problem is usually solved by resampling method. Therefore, this research was conducted with the aim of predicting the level of credit risk by implementing Random Over-Under Sampling in the Ensemble method using Decision Tree ID3, Random Forest, and Binary Logistics Regression. The data used is a dataset of credit card approval UCI Repository. The results showed that the Ensemble method has a better overall classification effectiveness level than others, as seen from the higher accuracy, precision, and fscore values, while the better classification effectiveness level in the form of recall is Binary Logistics Regression. Prediction classification using decision tree resulted in accuracy, precision and recall of 77.79%, 49.82, 45.95%, 47.76%, respectively. Prediction classification using random forest resulted in accuracy, precision and recall of 78.10%, 50.55%, 45.31%, 47.76%, respectively. Prediction classification using binary logistic regression resulted in accuracy, precision and recall of 74.16%, 42.66%, 48.90%, 45.55%, respectively. Prediction classification using ensemble majority vote resulted in accuracy, precision and recall of 78.22%, 50.86%, 45.54%, 48.03%, respectively. Keywords: Credit Risk, Ensemble, Decision Tree, Random Forest, Binary Logistics Regression

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Risiko Kredit, Ensemble, Decision Tree, Random Forest, Regresi Logistik Biner
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Fahira Anggraini
Date Deposited:	13 Jun 2022 08:15
Last Modified:	13 Jun 2022 08:15
URI:	http://repository.unsri.ac.id/id/eprint/72258

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