Internal Factor Analysis of Non-Performing Loans Using Multiple Linear Regression Method
Abstract
Loans are the largest source of income from banks compared to other sources of income. To ensure bank continuity, Bank income must exist from interest on loans, reaching almost 95% of all bank activities. For companies and banks that apply loan differences, loans are receivables which are cash that is delayed in receipt. Having problem loans can weaken a bank's financial condition. In general, two factors cause problems with loans, namely internal and external factors of the bank. Bank internal factors can be controlled by banks, compared to external factors, to prevent problem loans. Therefore, in this study, an analysis of internal factors affecting problem loans was carried out. The internal factors analyzed are the things that become the process and the essential part of the loan process, which includes loan supervision, acceptance procedures, and loan guarantees. This analysis is carried out to minimize the risk of non-performing loans caused by the inner side of the organization. The method used for analysis is using multiple linear regression analysis. Multiple linear regression analysis analyzed the relationship between the three independent variables (loan monitoring, acceptance procedures, and loan guarantees) and one dependent variable (non-performing loans). Multiple linear regression analysis provides predictions of the value of the dependent variable if the value of the independent variable increases or decreases and describes the direction of the relationship between the independent variable and the dependent variable, whether each independent variable is positively or negatively related. Based on the analysis results, the influence of loan monitoring factors, acceptance procedures, and loan guarantees on problem loans can be concluded that there is an influence between loan supervision and acceptance procedures on problem loans. At the same time, there is no effect between loan guarantees on problem loans.
Downloads
References
Brilliandy, E., Lucky, H., Hartanto, A., Suhartono, D., & Nurzaki, M. (2022). Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases. 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS), 310–315. https://doi.org/10.1109/AiDAS56890.2022.9918731
Dedić, F., Babović, E., Dizdar-Kapetanović, S., & Nogo, S. (2021). Regression Analysis of Dependency Between Related Courses on 1st Year of Study on Faculty of Information Technologies. 2021 20th International Symposium INFOTEH-JAHORINA (INFOTEH), 1–6. https://doi.org/10.1109/INFOTEH51037.2021.9400678
Hui, X., & Pang, S. (2012). The analysis of the operational efficiency of China’s commercial bank using DEA method and multiple regression analysis method. 2012 International Conference on Management Science & Engineering 19th Annual Conference Proceedings, 1303–1307. https://doi.org/10.1109/ICMSE.2012.6414343
Kasmir. (2014). Dasar-Dasar Perbankan Edisi Revisi. Raja Grafindo Persada.
Kavirathne, G. P. R. A., Perera, V. A. S., Karunathunge, L. C. R., Dewapura, B. N., Karunasena, A., & Pemadasa, M. G. N. M. (2022). A Meta-learning approach to Predict Non-performing Loans in Sri Lankan Financial Institutions. 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. https://doi.org/10.1109/ICCCNT54827.2022.9984519
Kireev, T., Kukartsev, V., Pilipenko, A., Rukosueva, A., & Suetin, V. (2022). Analysis of the Influence of Factors on Flight Delays in the United States Using the Construction of a Mathematical Model and Regression Analysis. 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 1–5. https://doi.org/10.1109/IEMTRONICS55184.2022.9795721
Luthfiarta, A., Zeniarja, J., Faisal, E., & Wicaksono, W. (2020). Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison. Journal of Applied Intelligent System, 4(2), 57–66. https://doi.org/10.33633/jais.v4i2.2772
Men, H., Zhang, S., Jin, J., & Xu, Z. (2009). Simultaneous Determination of Pb and Cd Ions with Ion Selective Electrodes Based on Multiple Linear Regression. 2009 Third International Symposium on Intelligent Information Technology Application, 1, 415–418. https://doi.org/10.1109/IITA.2009.69
Nursyahriana, A., Hadjat, M., & Tricahyadinata, I. (2017). Analisis Faktor Penyebab Terjadinya Kredit Macet. Forum Ekonomi, 19(1), 1. https://doi.org/10.29264/jfor.v19i1.2109
Serengil, S. I., Imece, S., Tosun, U. G., Buyukbas, E. B., & Koroglu, B. (2021). A Comparative Study of Machine Learning Approaches for Non Performing Loan Prediction. 2021 6th International Conference on Computer Science and Engineering (UBMK), 326–331. https://doi.org/10.1109/UBMK52708.2021.9558894
Setiyorini, T., & Frieyadie, F. (2022). Comparison of Linear Regressions and Neural Networks for Forecasting Covid-19 Recovered Cases. Jurnal Riset Informatika, 4(3), 277–282. https://doi.org/10.34288/jri.v4i3.409
Sugiyono. (2014). Metode Penelitian kuantitatif, kualitatif dan R & D. Alfabeta.
Xu, J., Gatpandan, P. H., Gatpandan, M. P., & Gao, Z. (2021). Freight and Passenger Volume Prediction using Multiple Regression. 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), 77–83. https://doi.org/10.1109/MLISE54096.2021.00022
Yang, L. (2021). Research on quantitative evaluation method of teachers based on multiple linear regression. 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 858–862. https://doi.org/10.1109/ICMTMA52658.2021.00196
Yani, J. A. (n.d.). Sugiyono. 2017. Metode Penelitian Kuantitatif, Kualitatif, Dan R&D. Bandung: Alfabeta. Ferrari, JR, Jhonson, JL, & McCown, WG (1995). Procrastination And Task Avoidance: Theory, Research & Treatment. New York: Plenum Press. Yudistira P, Chandra. Diktat Ku.


Copyright (c) 2023 Muhammad Irfandi, Fitria Fitria

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Jurnal Riset Informatika agrees to the following terms:
- The author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- The author is permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.