DOES THE COVID-19 PANDEMIC AFFECT FINANCIAL DISTRESS OF SHARIA COMMERCIAL BANKS IN INDONESIA? (ANALYSIS USING ARTIFICIAL NEURAL NETWORK)
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Sep 26, 2022
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Abstract
This study aims to determine whether there is a role for the current ratio value, return of equity, operating costs of operating income, company size, and the COVID-19 pandemic in the prediction model of financial distress for Islamic Commercial Banks (BUS) in Indonesia. This study uses a quantitative approach with non-parametric statistical methods artificial neural network backpropagation algorithm. The sample used is the financial report data of Islamic Commercial Banks (BUS) quarterly in 2015-2020 with the proportion of training data and testing data of 90%:10%, 80%:20%, and 70%:30%. The results of this study indicate the best model for predicting financial distress with the highest accuracy of 92.7% and AUC of 92.5%, which is included in the excellent classification. Of the five determinants of financial distress used, return of equity and company size contributed the most then BOPO, Current ratio and pandemic COVID-19 for the last. The COVID-19 pandemic only contributed 5. %. It confirms that Islamic banks in Indonesia can survive during the COVID-19 pandemic. Furthermore, Islamic banks need to maintain a stable return on equity and company size to minimize the chances of Islamic banks experiencing financial distress.
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MIRANTI, Titis; RAHMA, Dhea Asri; FITRIYAH, Fitriyah.
DOES THE COVID-19 PANDEMIC AFFECT FINANCIAL DISTRESS OF SHARIA COMMERCIAL BANKS IN INDONESIA? (ANALYSIS USING ARTIFICIAL NEURAL NETWORK).
Proceedings of the International Conference of Islamic Economics and Business (ICONIES), [S.l.], v. 8, n. 1, p. 65-76, sep. 2022.
ISSN 2541-3333.
Available at: <http://conferences.uin-malang.ac.id/index.php/iconies/article/view/1796>. Date accessed: 25 apr. 2024.
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References
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Azis, S. N., & Rahardjo, S. N. (2020). Analisis Faktor-FaktorYang Mempengaruhi Financial Distress Pada Perbankan Yang Terdaftar di Bursa Efek Indonesia. 07(02), 117–131.
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Candes, E. J., & Fine, T. L. (2000). Feedforward Neural Network Methodology. Journal of the American Statistical Association, 95(450), 682. https://doi.org/10.2307/2669423.
Chou, T.-K., & Buchdadi, A. D. (2016). Bank Performance and Its Underlying Factors: A Study of Rural Banks in Indonesia. Accounting and Finance Research, 5(3). https://doi.org/10.5430/afr.v5n3p55.
Dwijayanti, S. (2010). Penyebab, Dampak, Dan Prediksi Dari Financial Distress Serta Solusi Untuk Mengatasi Financial Distress. Jurnal Akuntansi Kontemporer, 2(2), 191–205.
Dwiyanti, R. F. (2017). Analisis Financial Distress Untuk Memprediksi Potensi Kebangkrutan Pada PT Krakatau Steel (Persero) Tbk.
Effendi, I., & Hariani, P. (2020). Dampak Covid-19 terhadap Bank Syariah : Impact of Covid-19 on Islamic Banks. EKONOMIKAWAN : Jurnal Ilmu Ekonomi Dan Studi Pembangunan, 20(79), 221–230.
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Masruri, M. T. (2020). Analisis Pengaruh ROA, FDR, BOPO Terhadap Financial Distress (Studi Kasus pada Bank Muamalat Indonesia Periode 2001-2019). Jurnal Ilmiah.
Miranti, T., Aulia, N. A., & Pimada, L. M. (2022). How the Covid-19 Outbreak Affect the Efficiency of Islamic Rural Banks? El Dinar, 10(1), 56–68. https://doi.org/10.18860/ed.v10i1.15577.
Nabila, D. (2020). Linear Discriminant Analysis Dalam Memprediksi Financial Distress Perbankan Syariah di Indonesia Periode 2011-2018.
Notolegowo, H. K. (2016). Analisis Determinan Non Performing Financing Bank Syariah di Indonesia Menggunakan Artificial Neural Network. SEMNAS FEKON 2016, 301–308.
Nugraha, D. A., & Nursito, N. (2021). Pengaruh Current Ratio, Debt To Equity Ratio, Dan Return On Equity Terhadap Financial Distress. Journal of Economic, Bussines and Accounting (COSTING), 4(2), 591–600. https://doi.org/10.31539/costing.v4i2.1699
Nursyamsu. (2016). Struktur Modal pada Perbankan Syariah. Bilancia, 10(1), 68–85.
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Paramitha, M. (2016). Perbandingan Metode Statistik dalam Memprediksi Sebuah Fenomena. Prosiding SNA MK, 233–242.
Platt, H. D., & Platt, M. B. (2002). Predicting Corporate Financial Distress: Reflections on Choice-based Sample Bias. Journal of Economics and Finance, 26(2), 184–199. https://doi.org/10.1007/bf02755985.
Porwati, V., Fasa, M. I., & Suharto. (2021). Analisis Potensi Profitabilitas Bank Syariah Pasca Merger Ditinjau Dari Determinan Yang Dapat Mempengaruhinya. Jurnal Manajemen Bisnis, 34(1), 34–41.
Pranita, K. R., & Kristanti, F. T. (2020). Analisis Financial Distress Menggunakan Analisis Survival. Nominal: Barometer Riset Akuntansi Dan Manajemen, 9(1), 62-79.
Pratiwi, A., Nurlita, B., Puspita, D., & Wahyudi, S. (2019). Pengujian Potensi Kebangkrutan Grup Bank Pembiayaan Rakyat Syariah di Indonesia The Assessment of Bankruptcy Potential of Sharia Rural Banks in Indonesia. Jurnal Economia, 15(1), 114–134.
Riauwanto, S., & Sulastiningsih, S. (2019). Pengaruh Total Aset Dan Bagi Hasil Perbankan Terhadap Volume Dana Pihak Ketiga (DPK) Pada Bank Umum Syariah. Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen, 6(2), 131–146. https://doi.org/10.32477/jrm.v6i2.354.
Rois, A. K., & Sugianto, D. (2021). Kekuatan Perbankan Syariah di Masa Krisis. Musyarakah: Journal of Islamic …, 1(1), 1–8.
Saifudin, A., & Wahono, S. (2015). Pendekatan Level Data untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. Journal of Software Engineering, 1(2), 76–85.
Saraswati, U. A. (2014). Prediksi Financial Distress Dengan Metode Neural Network. Artikel Ilmiah, 12–26.
Setyowati, W., & Sari, N. R. N. (2019). Pengaruh Likuiditas, Operating Capacity, Ukuran Perusahaan dan Pertumbuhan Penjualan terhadap Financial Distress. Jurnal Magisma, 7(2), 135–146.
Sholikah, A. M., & Miranti, T. (2020). Factors influence financial sustainability banking in Indonesia. Al-Tijary Jurnal Ekonomi Dan Bisnis Islam, 6(1), 41–50.
Siringoringo, R. (2018). Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE dan k-Nearest Neighbor. Jurnal ISD, 3(1), 44–49.
SPSS, I. (n.d.). IBM SPSS Neural Networks 22.
Theodorus, S., & Artini, L. G. S. (2018). Studi Financial Distress pada Perusahaan Perbankan di BEI. Jurnal Manajemen, 7(5), 2710–2732.
Wahyudi, R. (2020). Analisis Pengaruh CAR, NPF, FDR, BOPO dan Inflasi terhadap Profitabilitas Perbankan Syariah di Indonesia: Studi Masa Pandemi Covid-19. At-Taqaddum, 12(1), 13. https://doi.org/10.21580/at.v12i1.6093.
WHO. (2020). WHO Director-General’s Opening Remarks at The Media Briefing on COVID-19 - 11 March 2020.
Widati, L. W., & Prathama, B. A. (2015). Pengaruh Current Ratio, Debt To Equity Ratio, Dan Return On Equity Untuk Memprediksi Kondisi Financial Distress. Prosiding Seminar Nasional Multi Disiplin Ilmu & Call For Papers UNISBANK, 978–979.
Wijaya, A. H. (2019). Artificial Neural Network Untuk Memprediksi Beban Listrik Dengan Menggunakan Metode Backpropagation. Jurnal CoreIT, 5(2), 61–70.
Wijaya, R. R., Hapsari, D. W., & Kurnia. (2018). Pengaruh Rasio Camel Terhadap Financial Distress Pada Bank Umum Syariah Di Indonesia Periode 2011-2015. E-Proceeding of Management, 5(1), 786–795.
Wulandari, S. (2020). Analisis Pebgaruh Capital Adequacy Ratio (CAR), Financing Deposit Ratio (FDR), Non Performing Financing (NPF), Biaya Operasional Pendapatan Operasional (BOPO), dan Profitabilitas (ROA) Terhadap Financial Distress (Issue 63010160030). IAIN SALATIGA.
Z. Zacharis, N. (2016). Predicting Student Academic Performance in Blended Learning Using Artificial Neural Networks. International Journal of Artificial Intelligence & Applications, 7(5), 17–29. https://doi.org/10.5121/ijaia.2016.7502.
Zhafirah, A., & Majidah. (2019). Analisis Determinan Financial Distress. Analisis Determinan Financial Distress, 7(1), 195–202. https://doi.org/10.17509/jrak.v7i1.15497
Aminah, S., Rizal, N., & Taufiq, M. (2019). Pengaruh Rasio Camel Terhadap Financial Distress pada Sektor Perbankan. Progress Conference, 12(36), 332–345.
Aryadoust, V., & Goh, C. C. M. (2016). Predicting Listening Item Difficulty with Language Complexity Measures: A Comparative Data Mining Study Vahid. CaMLA Working Papers, 1(September), 1–16.
Azhari, A. R., & Wahyudi, R. (2020). Analisis Kinerja Perbankan Syariah di Indonesia : Studi Masa Pandemi Covid-19. Jurnal Ekonomi Syariah Indonesia), 10(2), 96–102.
Azis, S. N., & Rahardjo, S. N. (2020). Analisis Faktor-FaktorYang Mempengaruhi Financial Distress Pada Perbankan Yang Terdaftar di Bursa Efek Indonesia. 07(02), 117–131.
Barutu, M. J. S. (2019). Pengaruh Rasio Keuangan Terhadap Kondisi Financial Distress pada Perusahaan Subsektor Petambangan Batubara yang Terdaftar di BEI Periode 2014-2018. Universitas HKBP Nommensen.
Brahmana, R. K. (2007). Identifying Financial Distress Condition in Indonesia Manufacture Industry. Journal Business, 1–19.
Candes, E. J., & Fine, T. L. (2000). Feedforward Neural Network Methodology. Journal of the American Statistical Association, 95(450), 682. https://doi.org/10.2307/2669423.
Chou, T.-K., & Buchdadi, A. D. (2016). Bank Performance and Its Underlying Factors: A Study of Rural Banks in Indonesia. Accounting and Finance Research, 5(3). https://doi.org/10.5430/afr.v5n3p55.
Dwijayanti, S. (2010). Penyebab, Dampak, Dan Prediksi Dari Financial Distress Serta Solusi Untuk Mengatasi Financial Distress. Jurnal Akuntansi Kontemporer, 2(2), 191–205.
Dwiyanti, R. F. (2017). Analisis Financial Distress Untuk Memprediksi Potensi Kebangkrutan Pada PT Krakatau Steel (Persero) Tbk.
Effendi, I., & Hariani, P. (2020). Dampak Covid-19 terhadap Bank Syariah : Impact of Covid-19 on Islamic Banks. EKONOMIKAWAN : Jurnal Ilmu Ekonomi Dan Studi Pembangunan, 20(79), 221–230.
Exsanudin. (2014). Implementasi Jaringan Saraf Tiruan Backpropagation Untuk Estimasi Jumlah Produksi Gula (Studi Kasus PG Djombang Baru). UNIVERSITAS ISLAM NEGERI MAULANA MALIK IBRAHIM MALANG.
Geng, R., Bose, I., & Chen, X. (2015). Prediction of Financial Distress: An Empirical Study of Listed Chinese Companies Using Data Mining. In European Journal of Operational Research (Vol. 241, Issue 1). Elsevier B.V. https://doi.org/10.1016/j.ejor.2014.08.016.
Ihsan, D. N., & Hosen, M. N. (2021). Performance Bank Bni Syariah Di Masa Pandemi Covid-19. Jurnal Ilmiah Ekonomi Islam, 7(2), 756–770. https://doi.org/10.29040/jiei.v7i2.2494.
Julita, L. (2020). Sri Mulyani Bicara Dampak PSBB: Luar Biasa Serius! CNBC Indonesia.
Kartika, R., & Hasanudin, H. (2019). Analisis Pengaruh Likuiditas, Leverage, Aktivitas, Dan Profitabilitas Terhadap Financial Distress Pada Perusahaan Terbuka Sektor Infrastruktur, Utilitas, Dan Transportasi Periode 2011-2015. Oikonomia: Jurnal Manajemen, 15(1), 1–16. https://doi.org/10.47313/oikonomia.v15i1.640.
Kasmir. (2015). Analisis Laporan Keuangan. PT. Raja Grafindo Persada.
Kasmir. (2017). Analisis Laporan Keuangan. PT. Raja Grafindo Persada.
Lin, T. H. (2009). A Cross Model Study of Corporate Financial Distress Prediction in Taiwan: Multiple Discriminant Analysis, Logit, Probit and Neural Networks Models. Neurocomputing, 72(16–18), 3507–3516. https://doi.org/10.1016/j.neucom.2009.02.018.
Ling, C. X., Huang, J., & Zhang, H. (2003). AUC: A Better Measure Than Accuracy in Comparing Learning Algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2671, 329–341. https://doi.org/10.1007/3-540-44886-1_25.
Masruri, M. T. (2020). Analisis Pengaruh ROA, FDR, BOPO Terhadap Financial Distress (Studi Kasus pada Bank Muamalat Indonesia Periode 2001-2019). Jurnal Ilmiah.
Miranti, T., Aulia, N. A., & Pimada, L. M. (2022). How the Covid-19 Outbreak Affect the Efficiency of Islamic Rural Banks? El Dinar, 10(1), 56–68. https://doi.org/10.18860/ed.v10i1.15577.
Nabila, D. (2020). Linear Discriminant Analysis Dalam Memprediksi Financial Distress Perbankan Syariah di Indonesia Periode 2011-2018.
Notolegowo, H. K. (2016). Analisis Determinan Non Performing Financing Bank Syariah di Indonesia Menggunakan Artificial Neural Network. SEMNAS FEKON 2016, 301–308.
Nugraha, D. A., & Nursito, N. (2021). Pengaruh Current Ratio, Debt To Equity Ratio, Dan Return On Equity Terhadap Financial Distress. Journal of Economic, Bussines and Accounting (COSTING), 4(2), 591–600. https://doi.org/10.31539/costing.v4i2.1699
Nursyamsu. (2016). Struktur Modal pada Perbankan Syariah. Bilancia, 10(1), 68–85.
OJK. (2020a). Laporan Keuangan Bank Umum Syariah.
OJK. (2020b). Laporan Profil Industri Perbankan.
OJK. (2021). Statistik Perbankan Syariah Desember 2020.
Paramitha, M. (2016). Perbandingan Metode Statistik dalam Memprediksi Sebuah Fenomena. Prosiding SNA MK, 233–242.
Platt, H. D., & Platt, M. B. (2002). Predicting Corporate Financial Distress: Reflections on Choice-based Sample Bias. Journal of Economics and Finance, 26(2), 184–199. https://doi.org/10.1007/bf02755985.
Porwati, V., Fasa, M. I., & Suharto. (2021). Analisis Potensi Profitabilitas Bank Syariah Pasca Merger Ditinjau Dari Determinan Yang Dapat Mempengaruhinya. Jurnal Manajemen Bisnis, 34(1), 34–41.
Pranita, K. R., & Kristanti, F. T. (2020). Analisis Financial Distress Menggunakan Analisis Survival. Nominal: Barometer Riset Akuntansi Dan Manajemen, 9(1), 62-79.
Pratiwi, A., Nurlita, B., Puspita, D., & Wahyudi, S. (2019). Pengujian Potensi Kebangkrutan Grup Bank Pembiayaan Rakyat Syariah di Indonesia The Assessment of Bankruptcy Potential of Sharia Rural Banks in Indonesia. Jurnal Economia, 15(1), 114–134.
Riauwanto, S., & Sulastiningsih, S. (2019). Pengaruh Total Aset Dan Bagi Hasil Perbankan Terhadap Volume Dana Pihak Ketiga (DPK) Pada Bank Umum Syariah. Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen, 6(2), 131–146. https://doi.org/10.32477/jrm.v6i2.354.
Rois, A. K., & Sugianto, D. (2021). Kekuatan Perbankan Syariah di Masa Krisis. Musyarakah: Journal of Islamic …, 1(1), 1–8.
Saifudin, A., & Wahono, S. (2015). Pendekatan Level Data untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. Journal of Software Engineering, 1(2), 76–85.
Saraswati, U. A. (2014). Prediksi Financial Distress Dengan Metode Neural Network. Artikel Ilmiah, 12–26.
Setyowati, W., & Sari, N. R. N. (2019). Pengaruh Likuiditas, Operating Capacity, Ukuran Perusahaan dan Pertumbuhan Penjualan terhadap Financial Distress. Jurnal Magisma, 7(2), 135–146.
Sholikah, A. M., & Miranti, T. (2020). Factors influence financial sustainability banking in Indonesia. Al-Tijary Jurnal Ekonomi Dan Bisnis Islam, 6(1), 41–50.
Siringoringo, R. (2018). Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE dan k-Nearest Neighbor. Jurnal ISD, 3(1), 44–49.
SPSS, I. (n.d.). IBM SPSS Neural Networks 22.
Theodorus, S., & Artini, L. G. S. (2018). Studi Financial Distress pada Perusahaan Perbankan di BEI. Jurnal Manajemen, 7(5), 2710–2732.
Wahyudi, R. (2020). Analisis Pengaruh CAR, NPF, FDR, BOPO dan Inflasi terhadap Profitabilitas Perbankan Syariah di Indonesia: Studi Masa Pandemi Covid-19. At-Taqaddum, 12(1), 13. https://doi.org/10.21580/at.v12i1.6093.
WHO. (2020). WHO Director-General’s Opening Remarks at The Media Briefing on COVID-19 - 11 March 2020.
Widati, L. W., & Prathama, B. A. (2015). Pengaruh Current Ratio, Debt To Equity Ratio, Dan Return On Equity Untuk Memprediksi Kondisi Financial Distress. Prosiding Seminar Nasional Multi Disiplin Ilmu & Call For Papers UNISBANK, 978–979.
Wijaya, A. H. (2019). Artificial Neural Network Untuk Memprediksi Beban Listrik Dengan Menggunakan Metode Backpropagation. Jurnal CoreIT, 5(2), 61–70.
Wijaya, R. R., Hapsari, D. W., & Kurnia. (2018). Pengaruh Rasio Camel Terhadap Financial Distress Pada Bank Umum Syariah Di Indonesia Periode 2011-2015. E-Proceeding of Management, 5(1), 786–795.
Wulandari, S. (2020). Analisis Pebgaruh Capital Adequacy Ratio (CAR), Financing Deposit Ratio (FDR), Non Performing Financing (NPF), Biaya Operasional Pendapatan Operasional (BOPO), dan Profitabilitas (ROA) Terhadap Financial Distress (Issue 63010160030). IAIN SALATIGA.
Z. Zacharis, N. (2016). Predicting Student Academic Performance in Blended Learning Using Artificial Neural Networks. International Journal of Artificial Intelligence & Applications, 7(5), 17–29. https://doi.org/10.5121/ijaia.2016.7502.
Zhafirah, A., & Majidah. (2019). Analisis Determinan Financial Distress. Analisis Determinan Financial Distress, 7(1), 195–202. https://doi.org/10.17509/jrak.v7i1.15497