A Comparison of DEA CCR and DEA Aggressive in Determining The Efficiency of Indonesian Islamic Banking
Abstract
This study aims to measure the efficiency of processes in Islamic banking. Efficiency measurement needs to find out about the optimization of existing processes in Islamic banking. Efficiency assessment in Islamic banking can help in the risk management process and optimization of available resources. The main objective in a process is to maximize a number of input variables to produce a number of input variables. So the process needs to be measured and its efficiency seen. Because the main purpose of the efficiency measurement process is as evaluation material and as a basis for policies to maintain and improve process efficiency. The sample used in the study was monthly data from 2018-2022. To measure efficiency, data envelopment analysis method was used. The results of the analysis obtained were that in the DEA CCR analysis there were 35% of the total that did not achieve efficiency. In general, the DEA CCR results show that the 2022 period is the best period in achieving efficiency. Different results are shown in the DEA Aggressive that from all observed periods, the January 2022 period is the best period with an efficiency value of 2,26338.
References
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Witjaksono, A. (2013). Akuntansi Biaya. Graha Ilmu.
Zikri, Z., Ridho Kismawadi, E., & Hisan, K. (2021). Faktor-Faktor yang Mempengaruhi Net Operating Margin Pada BRI Syariah Periode Maret 2011-Maret 2019. JIM: Jurnal Ilmiah Mahasiswa, 3(1), 1–19. https://doi.org/10.32505/jim.v3i1.3289
Aminuddin, W. M. W. M., & Ismail, W. R. (2016). Integrated simulation and data envelopment analysis models in emergency department. AIP Conference Proceedings, 1784. https://doi.org/10.1063/1.4966822
Arsyad, L. (2008). Lembaga Keuangan Mikro. Andi Yogyakarta.
Boudaghi, E., & Farzipoor Saen, R. (2018). Developing a novel model of data envelopment analysis–discriminant analysis for predicting group membership of suppliers in sustainable supply chain. Computers and Operations Research, 89, 348–359. https://doi.org/10.1016/j.cor.2017.01.006
Cheng, G. Q., Wang, L., & Wang, Y. M. (2021). An extended three-stage dea model with interval inputs and outputs. International Journal of Computational Intelligence Systems, 14(1), 43–53. https://doi.org/10.2991/ijcis.d.201019.001
Dsilva Winfred Rufuss, D., Raj Kumar, V., Suganthi, L., Iniyan, S., & Davies, P. A. (2018). Techno-economic analysis of solar stills using integrated fuzzy analytical hierarchy process and data envelopment analysis. Solar Energy, 159(September 2017), 820–833. https://doi.org/10.1016/j.solener.2017.11.050
Dyson, R., Thanassoulis, E. (1988). Reducing Weight Flexibility in Data Envelopment Analysis. J Oper Res Soc, 39, 563–576. https://doi.org/https://doi.org/10.1057/jors.1988.96
Ertay, T., Ruan, D. (2005). Data Envelopment Analysis Based Decision Model For Optimal Operator Allocation In CMS. European Journal of Operational Research, 164(3), 800–810. https://doi.org/10.1016/j.ejor.2004.01.038
Istinfarani, S., & Azmi, F. (2020). Faktor Penentu Tingkat Efisiensi Kinerja Perbankan. Jurnal Akuntansi Dan Pajak, 20(2). https://doi.org/10.29040/jap.v20i2.800
John Doyle, R. G. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45, 567–578. https://doi.org/https://doi.org/10.1057/jors.1994.84
Kim, S., Kim, C., Kim, S., & Choi, S. (2022). Rationalization in Korea container terminal using DEA cross-efficiency and cluster analysis. Asian Journal of Shipping and Logistics, 38(2), 61–70. https://doi.org/10.1016/j.ajsl.2021.12.002
Lan, X., Li, Z., & Wang, Z. (2022). An investigation of the innovation efficacy of Chinese photovoltaic enterprises employing three-stage data envelopment analysis (DEA). Energy Reports, 8, 456–465. https://doi.org/10.1016/j.egyr.2022.05.093
Manopo, F. R. (2017). Pengaruh Variabel Ekonomi Makro terhadap Pertumbuhan Ekonomi Indonesia : Pendekatan Model Koreksi Kesalahan. Jurnal Ilmiah FEB UB, 53(9), 1–13.
Maruta, H. (2017). Akuntansi Aktiva Tetap Berwujud. JAS (Jurnal Akuntansi Syariah), 1(1), 63–97.
Miftahurrohman, M. (2019). Analisis Faktor-Faktor Yang Mempengaruhi Tingkat Efisiensi Perbankan Syariah Dengan Pendekatan Data Envelopment Analysis (Studi Pada Bank Syariah Negara-Negara ASEAN). Jurnal Lentera Akuntansi, 4(1), 71–91. https://doi.org/http://dx.doi.org/10.34127/jrakt.v4i1.282
Miranti, D. A., & Sari, K. (2016). Efisiensi Bank Umum Syariah Di Indonesia Menggunakan Pendekatan Data Envelopment Analysis (DEA). Jurnal Ilmiah Ekonomi Bisnis, 21(3), 194–200.
Muljawan, D., Hafidz, J., Astuti, R. I., & Oktapiani, R. (2014). Faktor-Faktor Penentu Efisiensi Perbankan Indonesia serta Dampaknya terhadap Perhitungan Suku Bunga Kredit. In Working Paper Bank Indonesia: Vol. WP/2/2014.
Mustafa, F. S., Khan, R. U., & Mustafa, T. (2021). Technical efficiency comparison of container ports in Asian and Middle East region using DEA. Asian Journal of Shipping and Logistics, 37(1), 12–19. https://doi.org/10.1016/j.ajsl.2020.04.004
Panuntun, B., & Sutrisno, S. (2018). Faktor Penentu Penyaluran Kredit Perbankan Studi Kasus Pada Bank Konvensional Di Indonesia. Urnal Riset Akuntansi Dan Keuangan Dewantara (JAD) , 1(2), 57–66.
Panuntun, B., & Sutrisno, S. (2019). Faktor Penentu Penyaluran Kredit Perbankan Studi Kasus Pada Bank Konvensional Di Indonesia. JAD : Jurnal Riset Akuntansi & Keuangan Dewantara, 1(2), 57–66. https://doi.org/10.26533/jad.v1i2.235
Pendharkar, P. C. (2021). Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems. Machine Learning with Applications, 6, 100092. https://doi.org/10.1016/j.mlwa.2021.100092
Řepková, I. (2015). Banking Efficiency Determinants in the Czech Banking Sector. Procedia Economics and Finance, 23(October 2014), 191–196. https://doi.org/10.1016/s2212-5671(15)00367-6
Ribeiro, M. C., Sousa, A. J., & Pereira, M. J. (2015). A Coregionalization Model to Assist the Selection Process of Local and Global Variables in Semi-parametric Geographically Weighted Poisson Regression. Procedia Environmental Sciences, 26, 53–56. https://doi.org/10.1016/j.proenv.2015.05.023
Sari, L. P. (2015). Analisis Pengaruh Rasio Keuangan Terhadap Pertumbuhan Laba (Studi Kasus : Perusahaan Food and Beverages yang Terdaftar di Bursa Efek Indonesia periode 2009 sampai dengan 2013). 4(4), 1–11.
Sugiyono, S. (2017). Metode Penelitian Kuantitatif Kualitatif dan R&D (25th ed.). ALFABETA.
Taliwuna, M. T., Saerang, D. P. ., & Murni, S. (2019). Analisis Pengaruh Faktor Internal Dan Eksternal Terhadap Roa Perbankan Di Indonesia. JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis Dan Inovasi Universitas Sam Ratulangi)., 6(3), 188–212. https://doi.org/10.35794/jmbi.v6i3.26681
Titko, J., & Jureviciene, D. (2014). DEA Application at Cross-country Benchmarking: Latvian vs Lithuanian Banking Sector. Procedia - Social and Behavioral Sciences, 110, 1124–1135. https://doi.org/10.1016/j.sbspro.2013.12.959
Toloo, M., & Nalchigar, S. (2009). A new integrated DEA model for finding most BCC-efficient DMU. Applied Mathematical Modelling, 33(1), 597–604. https://doi.org/10.1016/j.apm.2008.02.001
Witjaksono, A. (2013). Akuntansi Biaya. Graha Ilmu.
Zikri, Z., Ridho Kismawadi, E., & Hisan, K. (2021). Faktor-Faktor yang Mempengaruhi Net Operating Margin Pada BRI Syariah Periode Maret 2011-Maret 2019. JIM: Jurnal Ilmiah Mahasiswa, 3(1), 1–19. https://doi.org/10.32505/jim.v3i1.3289
Published
2024-12-18
How to Cite
Erdkhadifa, R., Mahmudah, S., Alfiki, R., & Munawwaroh, A. (2024). A Comparison of DEA CCR and DEA Aggressive in Determining The Efficiency of Indonesian Islamic Banking. Annual International Conference on Islamic Economics and Business (AICIEB), 4, 108-119. https://doi.org/https://doi.org/10.18326/aicieb.v4i0.678
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