How to cite this paper
Hidayat, Y., Sukono, S., Hartanto, P., Purwandari, T., Ibrahim, R., Saputra, M & Saputra, J. (2023). Integration of factor analysis and Tsukamoto’s fuzzy logic method for quality control of credit provisions in rural banks.Decision Science Letters , 12(2), 267-278.
Refrences
Adriyendi. (2018). Fuzzy Logic using Tsukamoto Model and Sugeno Model on Prediction Cost. International Journal of Intelligent Systems and Applications, 10(6), 13–21. https://doi.org/10.5815/ijisa.2018.06.02
Akhirina, T. Y., Rusmardiana, A., Yulistyanti, D., & Pauziah, U. (2019). The Comparison of K-Nearest Neighbor (K-NN) Algorithm and Fuzzy Tsukamoto Logic in the Determination of SMA Students Majors in Banten. Journal of Physics: Conference Series, 1175(1), 012068. https://doi.org/10.1088/1742-6596/1175/1/012068
Ardika, B. S., Setianingrum, A. H., & Hakiem, N. (2017). Funding eligibility decision support system using fuzzy logic Tsukamoto: (Case: BMT XYZ). 2017 Second International Conference on Informatics and Computing (ICIC), 1–7. https://doi.org/10.1109/IAC.2017.8280622
Bandyopadhyay, S., Mistri, H., Chattopadhyay, P., & Maji, B. (2013). Antenna array synthesis by implementing non-uniform amplitude using Tsukamoto fuzzy logic controller. 2013 International Conference on Advanced Electronic Systems (ICAES), 19–23. https://doi.org/10.1109/ICAES.2013.6659353
Bao, W., Lianju, N., & Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications, 128, 301–315. https://doi.org/10.1016/j.eswa.2019.02.033
Bartholomew, D. J. (1985). Foundations of factor analysis: Some practical implications. British Journal of Mathematical and Statistical Psychology, 38(1), 1–10. https://doi.org/10.1111/j.2044-8317.1985.tb00811.x
Brown, A. W., Kaiser, K. A., & Allison, D. B. (2018). Issues with data and analyses: Errors, underlying themes, and potential solutions. Proceedings of the National Academy of Sciences, 115(11), 2563–2570. https://doi.org/10.1073/pnas.1708279115
Corrigan, P. W., Salzer, M., Ralph, R. O., Sangster, Y., & Keck, L. (2004). Examining the Factor Structure of the Recovery Assessment Scale. Schizophrenia Bulletin, 30(4), 1035–1041. https://doi.org/10.1093/oxfordjournals.schbul.a007118
Fajri, D. M. N., Mahmudy, W. F., & Anggodo, Y. P. (2017). Optimization of FIS Tsukamoto using particle swarm optimization for dental disease identification. 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 261–268. https://doi.org/10.1109/ICACSIS.2017.8355044
Glaser, R. E. (1976). Exact Critical Values for Bartlett’s Test for Homogeneity of Variances. Journal of the American Statistical Association, 71(354), 488–490. https://doi.org/10.1080/01621459.1976.10480374
Lenka, S. R., Bisoy, S. K., Priyadarshini, R., Hota, J., & Barik, R. K. (2021). An Effective Credit Scoring Model Implementation by Optimal Feature Selection Scheme. 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 106–109. https://doi.org/10.1109/ESCI50559.2021.9396911
Liu, H., & Wang, N. (2021). Research on the Present Situation of Professional Identity of Young University Teachers Based on the KMO Sample Suitability Test and Bartlett Spherical Test. 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE), 366–369. https://doi.org/10.1109/ICBDIE52740.2021.00089
Llorca, M. (2017). External debt sustainability and vulnerabilities: evidence from a panel of 24 Asian countries and prospective analysis. ADBI Working Paper.
Ludvigson, S. C., & Ng, S. (2007). The empirical risk–return relation: A factor analysis approach☆. Journal of Financial Economics, 83(1), 171–222. https://doi.org/10.1016/j.jfineco.2005.12.002
McDade, T. W., & Adair, L. S. (2001). Defining the “urban” in urbanization and health: a factor analysis approach. Social Science & Medicine, 53(1), 55–70. https://doi.org/10.1016/S0277-9536(00)00313-0
Mhlanga, D. (2021). Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment. International Journal of Financial Studies, 9(3), 39. https://doi.org/10.3390/ijfs9030039
Miwakeichi, F., Martı́nez-Montes, E., Valdés-Sosa, P. A., Nishiyama, N., Mizuhara, H., & Yamaguchi, Y. (2004). Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis. NeuroImage, 22(3), 1035–1045. https://doi.org/10.1016/j.neuroimage.2004.03.039
Mkhaiber, A., & Werner, R. A. (2021). The relationship between bank size and the propensity to lend to small firms: New empirical evidence from a large sample. Journal of International Money and Finance, 110(5), 102281. https://doi.org/10.1016/j.jimonfin.2020.102281
Nugraha, E., Wibawa, A. P., Hakim, M. L., Kholifah, U., Dini, R. H., & Irwanto, M. R. (2019). Implementation of fuzzy tsukamoto method in decision support system of journal acceptance. Journal of Physics: Conference Series, 1280(2), 022031. https://doi.org/10.1088/1742-6596/1280/2/022031
Pan, S., Wei, J., & Pan, H. (2020). Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine. Scientific Programming, 2020, 1–7. https://doi.org/10.1155/2020/4561834
Saul, L. K., & Rahim, M. G. (2000). Maximum likelihood and minimum classification error factor analysis for automatic speech recognition. IEEE Transactions on Speech and Audio Processing, 8(2), 115–125. https://doi.org/10.1109/89.824696
Setyono, A., & Aeni, S. N. (2018). Development of Decision Support System for Ordering Goods using Fuzzy Tsukamoto. International Journal of Electrical and Computer Engineering (IJECE), 8(2), 1182. https://doi.org/10.11591/ijece.v8i2.pp1182-1193
Spuchľáková, E., Valašková, K., & Adamko, P. (2015). The Credit Risk and its Measurement, Hedging and Monitoring. Procedia Economics and Finance, 24, 675–681. https://doi.org/10.1016/S2212-5671(15)00671-1
Sudiyatno, S. I., & Wibowo, F. W. (2018). Fuzzy VRIO and THES based model of university competitive advantage. unpublished.
Suharjito, Diana, Yulyanto, & Nugroho, A. (2017). Mobile Expert System Using Fuzzy Tsukamoto for Diagnosing Cattle Disease. Procedia Computer Science, 116, 27–36. https://doi.org/10.1016/j.procs.2017.10.005
Velicer, W. F., & Jackson, D. N. (1990). Component Analysis versus Common Factor Analysis: Some issues in Selecting an Appropriate Procedure. Multivariate Behavioral Research, 25(1), 1–28. https://doi.org/10.1207/s15327906mbr2501_1
Wu, H.-C., Hu, Y.-H., & Huang, Y.-H. (2014). Two-stage credit rating prediction using machine learning techniques. Kybernetes, 43(7), 1098–1113. https://doi.org/10.1108/K-10-2013-0218
Wu, M., Cheng, G., & Gao, J. (2021). Research on the Measurement of Subject Credit Risk of Chinese Port Enterprises by Constrained Logistic Regression. Asia-Pacific Journal of Operational Research, 38(03), 2040016. https://doi.org/10.1142/S0217595920400163
Xiao, J. J., & Wu, G. (2008). Completing debt management plans in credit counseling: An application of the theory of planned behavior. Journal of Financial Counseling and Planning, 19(2).
Yang, S., & Islam, M. T. (2020). Principal component analysis and factor analysis for feature selection in credit rating. ArXiv Preprint ArXiv:2011.09137, 09137.
Yangyudongnanxin, G. (2021). Financial Credit Risk Control Strategy Based on Weighted Random Forest Algorithm. Scientific Programming, 2021, 1–9. https://doi.org/10.1155/2021/6276155
Yi, G., Lei, H., & Ziqiang, L. (2015). Port Customer Credit Risk Prediction Based on Internal and External Information Fusion. The Open Cybernetics & Systemics Journal, 9(1), 1323–1328. https://doi.org/10.2174/1874110X01509011323
Yu, L., Wang, S., & Lai, K. K. (2009). An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring. European Journal of Operational Research, 195(3), 942–959. https://doi.org/10.1016/j.ejor.2007.11.025
Akhirina, T. Y., Rusmardiana, A., Yulistyanti, D., & Pauziah, U. (2019). The Comparison of K-Nearest Neighbor (K-NN) Algorithm and Fuzzy Tsukamoto Logic in the Determination of SMA Students Majors in Banten. Journal of Physics: Conference Series, 1175(1), 012068. https://doi.org/10.1088/1742-6596/1175/1/012068
Ardika, B. S., Setianingrum, A. H., & Hakiem, N. (2017). Funding eligibility decision support system using fuzzy logic Tsukamoto: (Case: BMT XYZ). 2017 Second International Conference on Informatics and Computing (ICIC), 1–7. https://doi.org/10.1109/IAC.2017.8280622
Bandyopadhyay, S., Mistri, H., Chattopadhyay, P., & Maji, B. (2013). Antenna array synthesis by implementing non-uniform amplitude using Tsukamoto fuzzy logic controller. 2013 International Conference on Advanced Electronic Systems (ICAES), 19–23. https://doi.org/10.1109/ICAES.2013.6659353
Bao, W., Lianju, N., & Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications, 128, 301–315. https://doi.org/10.1016/j.eswa.2019.02.033
Bartholomew, D. J. (1985). Foundations of factor analysis: Some practical implications. British Journal of Mathematical and Statistical Psychology, 38(1), 1–10. https://doi.org/10.1111/j.2044-8317.1985.tb00811.x
Brown, A. W., Kaiser, K. A., & Allison, D. B. (2018). Issues with data and analyses: Errors, underlying themes, and potential solutions. Proceedings of the National Academy of Sciences, 115(11), 2563–2570. https://doi.org/10.1073/pnas.1708279115
Corrigan, P. W., Salzer, M., Ralph, R. O., Sangster, Y., & Keck, L. (2004). Examining the Factor Structure of the Recovery Assessment Scale. Schizophrenia Bulletin, 30(4), 1035–1041. https://doi.org/10.1093/oxfordjournals.schbul.a007118
Fajri, D. M. N., Mahmudy, W. F., & Anggodo, Y. P. (2017). Optimization of FIS Tsukamoto using particle swarm optimization for dental disease identification. 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 261–268. https://doi.org/10.1109/ICACSIS.2017.8355044
Glaser, R. E. (1976). Exact Critical Values for Bartlett’s Test for Homogeneity of Variances. Journal of the American Statistical Association, 71(354), 488–490. https://doi.org/10.1080/01621459.1976.10480374
Lenka, S. R., Bisoy, S. K., Priyadarshini, R., Hota, J., & Barik, R. K. (2021). An Effective Credit Scoring Model Implementation by Optimal Feature Selection Scheme. 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 106–109. https://doi.org/10.1109/ESCI50559.2021.9396911
Liu, H., & Wang, N. (2021). Research on the Present Situation of Professional Identity of Young University Teachers Based on the KMO Sample Suitability Test and Bartlett Spherical Test. 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE), 366–369. https://doi.org/10.1109/ICBDIE52740.2021.00089
Llorca, M. (2017). External debt sustainability and vulnerabilities: evidence from a panel of 24 Asian countries and prospective analysis. ADBI Working Paper.
Ludvigson, S. C., & Ng, S. (2007). The empirical risk–return relation: A factor analysis approach☆. Journal of Financial Economics, 83(1), 171–222. https://doi.org/10.1016/j.jfineco.2005.12.002
McDade, T. W., & Adair, L. S. (2001). Defining the “urban” in urbanization and health: a factor analysis approach. Social Science & Medicine, 53(1), 55–70. https://doi.org/10.1016/S0277-9536(00)00313-0
Mhlanga, D. (2021). Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment. International Journal of Financial Studies, 9(3), 39. https://doi.org/10.3390/ijfs9030039
Miwakeichi, F., Martı́nez-Montes, E., Valdés-Sosa, P. A., Nishiyama, N., Mizuhara, H., & Yamaguchi, Y. (2004). Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis. NeuroImage, 22(3), 1035–1045. https://doi.org/10.1016/j.neuroimage.2004.03.039
Mkhaiber, A., & Werner, R. A. (2021). The relationship between bank size and the propensity to lend to small firms: New empirical evidence from a large sample. Journal of International Money and Finance, 110(5), 102281. https://doi.org/10.1016/j.jimonfin.2020.102281
Nugraha, E., Wibawa, A. P., Hakim, M. L., Kholifah, U., Dini, R. H., & Irwanto, M. R. (2019). Implementation of fuzzy tsukamoto method in decision support system of journal acceptance. Journal of Physics: Conference Series, 1280(2), 022031. https://doi.org/10.1088/1742-6596/1280/2/022031
Pan, S., Wei, J., & Pan, H. (2020). Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine. Scientific Programming, 2020, 1–7. https://doi.org/10.1155/2020/4561834
Saul, L. K., & Rahim, M. G. (2000). Maximum likelihood and minimum classification error factor analysis for automatic speech recognition. IEEE Transactions on Speech and Audio Processing, 8(2), 115–125. https://doi.org/10.1109/89.824696
Setyono, A., & Aeni, S. N. (2018). Development of Decision Support System for Ordering Goods using Fuzzy Tsukamoto. International Journal of Electrical and Computer Engineering (IJECE), 8(2), 1182. https://doi.org/10.11591/ijece.v8i2.pp1182-1193
Spuchľáková, E., Valašková, K., & Adamko, P. (2015). The Credit Risk and its Measurement, Hedging and Monitoring. Procedia Economics and Finance, 24, 675–681. https://doi.org/10.1016/S2212-5671(15)00671-1
Sudiyatno, S. I., & Wibowo, F. W. (2018). Fuzzy VRIO and THES based model of university competitive advantage. unpublished.
Suharjito, Diana, Yulyanto, & Nugroho, A. (2017). Mobile Expert System Using Fuzzy Tsukamoto for Diagnosing Cattle Disease. Procedia Computer Science, 116, 27–36. https://doi.org/10.1016/j.procs.2017.10.005
Velicer, W. F., & Jackson, D. N. (1990). Component Analysis versus Common Factor Analysis: Some issues in Selecting an Appropriate Procedure. Multivariate Behavioral Research, 25(1), 1–28. https://doi.org/10.1207/s15327906mbr2501_1
Wu, H.-C., Hu, Y.-H., & Huang, Y.-H. (2014). Two-stage credit rating prediction using machine learning techniques. Kybernetes, 43(7), 1098–1113. https://doi.org/10.1108/K-10-2013-0218
Wu, M., Cheng, G., & Gao, J. (2021). Research on the Measurement of Subject Credit Risk of Chinese Port Enterprises by Constrained Logistic Regression. Asia-Pacific Journal of Operational Research, 38(03), 2040016. https://doi.org/10.1142/S0217595920400163
Xiao, J. J., & Wu, G. (2008). Completing debt management plans in credit counseling: An application of the theory of planned behavior. Journal of Financial Counseling and Planning, 19(2).
Yang, S., & Islam, M. T. (2020). Principal component analysis and factor analysis for feature selection in credit rating. ArXiv Preprint ArXiv:2011.09137, 09137.
Yangyudongnanxin, G. (2021). Financial Credit Risk Control Strategy Based on Weighted Random Forest Algorithm. Scientific Programming, 2021, 1–9. https://doi.org/10.1155/2021/6276155
Yi, G., Lei, H., & Ziqiang, L. (2015). Port Customer Credit Risk Prediction Based on Internal and External Information Fusion. The Open Cybernetics & Systemics Journal, 9(1), 1323–1328. https://doi.org/10.2174/1874110X01509011323
Yu, L., Wang, S., & Lai, K. K. (2009). An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring. European Journal of Operational Research, 195(3), 942–959. https://doi.org/10.1016/j.ejor.2007.11.025