Deteksi Peluang Gagal Bayar Calon Debitur Menggunakan Algoritma Particle Swarm Optimization (PSO) untuk Meningkatkan Kinerja Manajemen Risiko pada Koperasi Simpan Pinjam ABC

Susan Purnama, Aninditha Putri Kusumawardhani

Abstract


Savings and Loan Cooperatives (KSP) are financial institutions that have an important role in economic and trade activities, useful for channeling funds in the form of loans to members who need them for business or business. In this paper, we examine the detection of potential debtors' default opportunities using the Particle Swarm Optimization (PSO) algorithm in a logistic regression model. In the analysis method, there are several steps: (1) standardizing the data on the risk factor data of prospective debtors, (2) determining the assumptions of the logistic regression model, (3) estimating the parameters of the logistic regression model using the Particle Swarm Optimization (PSO) algorithm, and (4 ) to test the significance of each variable. The probability of default is determined using the eligibility parameters of the prospective debtor based on past data variables owned by KSP "ABC" in Bandung, Indonesia. The results show that of the eight factors analyzed, there are six factors that have a significant influence on the risk of default, namely the age of the debtor, the number of family dependents, the amount of savings, the amount of collateral, the amount of credit, the credit period with an accuracy of 99.1%. Based on these six factors, a logistic regression model estimator is obtained that can be used to determine the probability of default from prospective debtors. This probability of default is very useful for KSP "ABC" to make a decision on whether or not to give credit, so that the performance of problem loan risk management can be guaranteed.


Keywords


Savings and Loan Cooperatives, risk of non-performing loans, probability of default, logistic regression model, Particle Swarm Optimization (PSO).

References


S. Purwantini, E. Rusdianti and P. Wardoyo. “Kajian Pengelolaan Dana Koperasi Simpan Pinjam Konvensional Di Kota Semarang”. Jurnal Dinamika Sosial Budaya, 18:133-145, 2017.

N. Tamin. “Kiat Menghindari Kredit Macet”. Jakarta: Dian Rakyat, 2012.

T. Harris. “Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions”. Expert Systems with Applications, 40:4404-4413, 2013.

D. A. D. M.Putri, N. L. G. E. Sulindawati, S. E. Ak and I. N. P. Yasa. “Analisis Tingkat Kesehatan Koperasi Simpan Pinjam (Ksp) Di Kabupaten Buleleng Berdasarkan Peraturan Menteri No. 14/Per/M. Kukm/Xii/2009”. Jimat (Jurnal Ilmiah Mahasiswa Akuntansi) Undiksha, 8, 2018.

D. Martens, L. Bruynseels, B. Baesens, M. Willekens and J. Vanthienen. “Predicting going concern opinion with data mining”. Decision Support Systems, 45:765-777, 2008.

J. Kennedy and R. Eberhart. “Particle swarm optimization”. In Proceedings of ICNN'95-international conference on neural networks, 4:1942-1948, 1995.

H. C. Koh, W. C. Tan and C. P. Goh. “A two-step method to construct credit scoring models with data mining techniques”. International Journal of Business and Information, 1:96-118, 2006.

A. E. Khandani, A. J. Kim and A. W. Lo. “Consumer credit-risk models via machine-learning algorithms”. Journal of Banking & Finance, 34:2767-2787, 2010.

H. Sabzevari, M. Soleymani, and E. Noorbakhsh. “A comparison between statistical and data mining methods for credit scoring in case of limited available data”. In Proceedings of the 3rd CRC Credit Scoring Conference, 1-5, 2007.

A. Bagheri, H. M. Peyhani and M. Akbari. “Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization”. Expert Systems with Applications, 41:6235-6250, 2014.

A. Blanco, R. Pino-Mejías, J. Lara, and S. Rayo. “Credit scoring models for the microfinance industry using neural networks: Evidence from Peru”. Expert Systems with applications, 40:356-364, 2013.

A. Samreen, F. B. Zaidi and A. Sarwar. “Design and Development of Credit Scoring Model for the Commercial Banks in Pakistan: Forecasting Creditworthiness of Corporate Borrowers”. International Journal of Business and Commerce, 2:1-26, 2013.

A. Samreen and F. B. Zaidi. “Design and development of credit scoring model for the commercial banks of Pakistan: Forecasting creditworthiness of individual borrowers”. International Journal of Business and Social Science, 3, 2012.

W. Munyanyi and T. Mashamba. “Banks Business Models, Risk Management Systems And Small And Medium Enterprises Financing Proclivity In Zimbabwe”. Journal of Management and Economic Studies, 1:16-33, 2019.

I. Kholid. “Penilaian Kesehatan Koperasi Simpan Pinjam Berdasarkan Peraturan Menteri Koperasi dan Usaha Kecil dan Menengah Republik Indonesia Nomor 14/Per/M. Kukm/Xii/2009 (Studi Pada Koperasi Simpan Pinjam Adi Wiyata Mandiri Kab. Blitar)”. Jurnal administrasi bisnis, 15, 2014

D. S. Lestari and A. E. Handayani. “Penerapan Audit Laporan Keuangan Bagi Pengelola Koperasi di Kabupaten Madiun”. JHP17: Jurnal Hasil Penelitian, 4, 2019.

E. Demidenko. “Sample size determination for logistic regression revisited”. Statistics in medicine, 26:3385-3397, 2017.

M. R. C. Acosta, S. Ahmed, C. E. Garcia and I Koo. “Extremely randomized trees-based scheme for stealthy cyber-attack detection in smart grid networks”. IEEE access, 8, 19921-19933, 2020.

S. L. Gortmaker. “Theory and methods--Applied Logistic Regression by David W. Hosmer Jr and Stanley Lemeshow ''. Contemporary sociology, 23:59, 1994.

N. B. Nawai, and M. N. B. M. Shariff. “Determinants of repayment performance in microfinance programs in Malaysia”. Labuan Bulletin of International Business and Finance (LBIBF), 14-29, 2013.

S. A. Czepiel. “Maximum likelihood estimation of logistic regression models: theory and implementation”. Available at czep. net/stat/miller. pdf, 83, 2020.

A. S. Sukono, M. Mamat, and K. Prafidya. “Credit scoring for cooperative financial services using logistic regression estimated by genetic algorithm”. Applied Mathematical Sciences, 8:45-57, 2014.

R. F. Malik. “Credit Scoring Using CART Algorithm and Binary Particle Swarm Optimization”. International Journal of Electrical & Computer Engineering (2088-8708), 8, 2018.

Y. Guo, J. He, L. Xu, and W. Liu. “A novel multi-objective particle swarm optimization for comprehensible credit scoring”. Soft Computing, 23:9009-9023, 2019.

F. Barboza H. Kimura and E. Altman. “Machine learning models and bankruptcy prediction”. Expert Systems with Applications, 83:405-417, 2017.

M. T. Joseph, G. Edson, F. Manure, M. Clifford and K. Michael. “Non performing loans in commercial banks: a case of CBZ Bank Limited in Zimbabwe ''. Interdisciplinary Journal of Contemporary Research in Business, 4:467-488, 2012.

B. N. Ruchjana, A. T. Arianto, K. Parmikanti and B. Suhandi. “Peramalan Konsentrasi Particulate Matter 2.5 (PM2. 5) menggunakan Model Vector Autoregressive dengan Metode Maximum Likelihood Estimation.” KUBIK: Jurnal Publikasi Ilmiah Matematika, 6: 1-12, 2021.

C. Rahmadayanti, H. Rabbani and A. A. Rohmawati. “Model Autoregressive dengan Pendekatan Conditional Maximum Likelihood Untuk Prediksi Harga Saham.” KUBIK: Jurnal Publikasi Ilmiah Matematika, 3: 52-59, 2018.

M. Mahdi and M. Khaddafi. “The Influence of Gross Profit Margin, Operating Profit Margin and Net Profit Margin on the Stock Price of Consumer Good Industry in the Indonesia Stock Exchange in 2012-2014.” International Journal of Business, Economics, and Social Development, 1: 153-163, 2020.

E. S. Hasbullah, E. Rusyaman and A. Kartiwa. “The GARCH Model Volatility of Sharia Stocks Associated Causality with Market Index.” International Journal of Quantitative Research and Modeling, 1: 18-28, 2020.




DOI: https://doi.org/10.15575/kubik.v6i2.13835

Refbacks

  • There are currently no refbacks.


Journal KUBIK: Jurnal Publikasi Ilmiah Matematika has indexed by