Prediction of Credit Eligibility Using the Random Forest Method
Prediksi Kelayakan Pemberian Kredit Menggunakan Metode Random Forest
DOI:
https://doi.org/10.21070/ups.3515Keywords:
Credit Worthiness, Outlier, Prediction Model, Random ForestAbstract
This research uses data from Kaggle, which consists of 32,581 rows and 12 columns, to develop a credit worthiness prediction model. The aim of the research is to identify factors that influence creditworthiness and develop a model that accurately predicts whether a borrower is creditworthy or not. The research uses the Random Forest method and involves data pre-processing steps, including imputation of missing values and handling of outliers, as well as dividing the dataset into training data and test data. The results show that the model achieves an accuracy of 93.28%, with the best parameters 'max_depth': 30, 'min_samples_leaf': 1, 'min_samples_split': 2, and 'n_estimators': 100. This research contributes to the understanding of creditworthiness and development Prediction models that can be used by financial institutions to make more precise credit decisions.
Downloads
References
Nasional, J., & Informasi, S. (2021). Analisa Prediksi Kelayakan Pemberian Kredit Pinjaman dengan Metode Random. Jurnal Nasional Teknologi Dan Sistem Informasi, 02, 79–89.R. Hayes, G. Pisano, D. Upton, and S. Wheelwright, Operations, Strategy, and Technology: Pursuing the competitive edge. Hoboken, NJ: Wiley, 2005.
Prasetyo Tarigan, D., Wantoro, A., & Abidin, Z. (2020). Sistem Pendukung Keputusan Pemberian Kredit Mobil Dengan Fuzzy Tsukamoto(Studi Kasus : Pt Clipan Finance). TELEFORTECH : Journal of Telematics and Information Technology, 1(1), 32–37. https://doi.org/10.33365/tft.v1i1.870.A. Rezi and M. Allam, "Techniques in array processing by means of transformations, " in Control and Dynamic Systems, Vol. 69, Multidemsional Systems, C. T. Leondes, Ed. San Diego: Academic Press, 1995, pp. 133-180.
M. Rizki, M. Isnaini, H. Umam, and M. L. Hamzah, “Aplikasi Data Mining Dengan Metode CHAID Dalam Menentukan Status Kredit,” Jurnal Sains, Teknologi dan Industri, vol. 18, no. 1, pp. 29–33, 2020.
Syafi’i, O. Nurdiawan, and G. Dwilestari, “Penerapan Machine Learning Untuk Menentukan Kelayakan Kredit Menggunakan Metode Support Vektor Machine,” Jurnal Sistem Informasi dan Manajemen, vol. 10, no. 2, 2022.
Darmawan, Z. M. E., & Fauzan Dianta, A. (2023). Implementasi Optimasi Hyperparameter GridSearchCV Pada Sistem Prediksi Serangan Jantung Menggunakan SVM. Online) Teknologi: Jurnal Ilmiah Sistem Informasi, 13(1), 8–15. Retrieved from https://doi.org/10.26594/teknologi.v13i1.3098Tersediaonlinediwww.journal.unipdu.ac.idHalamanjurnaldiwww.journal.unipdu.ac.id/index.php/teknologi.
Obey Al Farobi. (2021). Implementasi Metode Support Vector Machine ( Svm ) Untuk Mengetahui Respon Masyarakat Indonesia Terhadap Implementasi Metode Support Vector Machine ( Svm ) Untuk Mengetahui Respon Masyarakat Indonesia Terhadap. Repository.Uinjkt.Ac.Id, 82. Retrieved from https://repository.uinjkt.ac.id/dspace/handle/123456789/65151%0Ahttps://repository.uinjkt.ac.id/dspace/bitstream/123456789/65151/1/OBEY AL FAROBI-FST.pdf
Yuhana, U. L., & Purwarianti, A. (2022). Tuning Hyperparameter pada Gradient Boosting. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 8(1), 134–139.
Rahayu, W. I., Prianto, C., & Novia, E. A. (2021). Perbandingan Algoritma K-Means Dan Naïve Bayes Untuk Memprediksi Prioritas Pembayaran Tagihan Rumah Sakit Berdasarkan Tingkat Kepentingan Pada Pt. Pertamina (Persero). Jurnal Teknik Informatika, 13(2), 1–8. Retrieved from https://ejurnal.poltekpos.ac.id/index.php/informatika/article/view/1383
Mercifia, M., & Averina Anasthasya. (2022). 268-890-1-Pb. Jurnal Ilmiah NERO, 7(1), 2–2.
Iriananda, S. W., Putra, R. P., & Nugroho, K. S. (2021). Analisis Sentimen Dan Analisis Data Eksploratif Ulasan Aplikasi Marketplace Google Playstore. The 4th Conference on Innovation and Application of Science and Technology (CIASTECH 2021), (Ciastech), 473–482
Downloads
Additional Files
Posted
License
Copyright (c) 2023 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
