A Backpropagation Artificial Neural Network Approach for Loan Status Prediction
Abstract
Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors. It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.
Keywords
Full Text:
PDFReferences
R. Indonesia, “Undang-Undang RI No. 10 Tahun 1998 tentang Perbankan,” Lembaran Negara Republik Indonesia., 1998, [Online]. Available: http://www.bphn.go.id/data/documents/98uu010.pdf.
A. Hornby and D. Lea, Oxford Advanced Learner’s Dictionary of Current English, 10th editi. Oxford: Oxford University Press, 2020.
S. Heffernan, Modern Banking, vol. 16, no. 3. 2016.
Christabell. "Prediction of loan status using logistiscs regression model and naïve bayes classifier", Thesis undergraduate, Presiden University,
S. M. Fati, “Machine Learning-Based Prediction Model for Loan Status Approval,” J. Hunan Univ. Nat. Sci., vol. 48, no. 10, 2021, [Online]. Available: http://jonuns.com/index.php/journal/article/view/783.
M. A. Sheikh, A. K. Goel, and T. Kumar, “An Approach for Prediction of Loan Approval using Machine Learning Algorithm,” Proc. Int. Conf. Electron. Sustain. Commun. Syst. ICESC 2020, no. Icesc, pp. 490–494, 2020, doi: 10.1109/ICESC48915.2020.9155614.
C. Mason, J. Twomey, D. Wright, and L. Whitman, “Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression,” Res. High. Educ., vol. 59, no. 3, pp. 382–400, 2018, doi: 10.1007/s11162-017-9473-z.
M. R. Romadhon and F. Kurniawan, “A Comparison of Naive Bayes Methods, Logistic Regression and KNN for Predicting Healing of Covid-19 Patients in Indonesia,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 41–44, 2021, doi: 10.1109/EIConCIT50028.2021.9431845.
L. Zhu, D. Qiu, D. Ergu, C. Ying, and K. Liu, “A study on predicting loan default based on the random forest algorithm,” Procedia Comput. Sci., vol. 162, no. Itqm 2019, pp. 503–513, 2019, doi: 10.1016/j.procs.2019.12.017.
S. Hassanipour et al., “Comparison of artificial neural network and logistic regression models for prediction of outcomes in trauma patients: A systematic review and meta-analysis,” Injury, vol. 50, no. 2, pp. 244–250, Feb. 2019, doi: 10.1016/j.injury.2019.01.007.
A. Al Imran, M. N. Amin, and F. T. Johora, “Classification of Chronic Kidney Disease using Logistic Regression, Feedforward Neural Network and Wide & Deep Learning,” in 2018 International Conference on Innovation in Engineering and Technology (ICIET), Dec. 2018, pp. 1–6, doi: 10.1109/CIET.2018.8660844.
L. Zajmi, F. Y. H. Ahmed, and A. A. Jaharadak, “Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks,” Appl. Comput. Intell. Soft Comput., vol. 2018, pp. 1–7, Sep. 2018, doi: 10.1155/2018/9547212.
A. K. Jana, “R-Machine-Learning,” GitHub, Inc., 2018. https://github.com/anup-jana/R-Machine-Learning/tree/master/R Scripts/Datasets.
J. Han, J. Pei, and M. Kamber, Data Mining: Concepts and Techniques. S.I.: Morgan Kaufmann, 2022.
M. Kuhn and K. Johnson, Applied Predictive Modeling. New York, NY: Springer New York, 2019.
T. U. Islam and M. Rizwan, “Comparison of correlation measures for nominal data,” Commun. Stat. - Simul. Comput., vol. 51, no. 3, pp. 698–714, Mar. 2022, doi: 10.1080/03610918.2020.1869984.
T. Hailemeskel Abebe, “The Derivation and Choice of Appropriate Test Statistic (Z, t, F and Chi-Square Test) in Research Methodology,” Math. Lett., vol. 5, no. 3, pp. 33–40, 2019, doi: 10.11648/j.ml.20190503.11.
C. Uakarn, K. Chaokromthong, and N. Sintao, “Sample Size Estimation using Yamane and Cochranand Krejcie and Morgan and Green Formulas and Cohen Statistical Power Analysis by G*Power and Comparisons,” APHEIT Int. J., vol. 10 No. 2, pp. 76–88, 2021, [Online]. Available: https://so04.tci-thaijo.org/index.php/ATI/article/view/254253/173847.
A. P. Windarto et al., Jaringan Saraf Tiruan: Algoritma Prediksi dan Implementasi. Yayasan Kita Menulis, 2020.
J. Feng and S. Lu, “Performance Analysis of Various Activation Functions in Artificial Neural Networks,” J. Phys. Conf. Ser., vol. 1237, no. 2, 2019, doi: 10.1088/1742-6596/1237/2/022030.
A. Jain, A. Fandago, and A. Kapoor, TensorFlow Machine Learning Projects. Packt Publishing, 2018.
S. D. Desai, S. Giraddi, P. Narayankar, N. R. Pudakalakatti, and S. Sulegaon, Back-propagation neural network versus logistic regression in heart disease classification, vol. 702. Springer Singapore, 2019.
DOI: https://doi.org/10.15408/jti.v15i2.27006 Abstract - 0 PDF - 0
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Gabrielle Jovanie Sitepu, Edwin Setiawan Nugraha
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
3rd Floor, Dept. of Informatics, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No.95, Cempaka Putih, Ciputat Timur.
Kota Tangerang Selatan, Banten 15412
Tlp/Fax: +62 21 74019 25/ +62 749 3315
Handphone: +62 8128947537
E-mail: jurnal-ti@apps.uinjkt.ac.id
Jurnal Teknik Informatika by Prodi Teknik Informatika Universitas Islam Negeri Syarif Hidayatullah Jakarta is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at http://journal.uinjkt.ac.id/index.php/ti.
JTI Visitor Counter: View JTI Stats