Efficiency of Conventional Versus Islamic Commercial Banks in Indonesia 2014-2016 Using Data Envelopment Analysis
Hijri Juliansyah1, Cut Putri Mellita Sari2, Devi Andriyani3, Tarmizi Abbas4, Ilham Sanusi5
1Hijri Juliansyah, Faculty of Economics and Business, Universitas Malikussaleh, Indonesia.
2Cut Putri Mellita Sari, Faculty of Economics and Business, Universitas Malikussaleh, Indonesia.
3Devi Andriyani, Faculty of Economics and Business, Universitas Malikussaleh, Indonesia.
4Tarmizi Abbas, Faculty of Economics and Business, Universitas Malikussaleh, Indonesia.
5Ilham Sanusi, Faculty of Economics and Business, Universitas Malikussaleh, Indonesia.
Manuscript received on 09 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 1436-1439 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F12530476S519/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study focuses on efficiency of Islamic versus Conventioal Commercial Banks. The study uses Data Envelopment Analysis (DEA) to determine efficiency of productivity growth in banks registered by applying input output orientation Constant Return to Scale (CRS) and Variable Return to Scale (VRS) during 2014-2016. Input variables that used to measure this relative efficiency are Current Account, Deposits and Savings. While output variables used in this research are Financing and Total Assets. In the DEA technique, efficiency is measured by CRS and VRS indexes. The results of the study using CRS show that on average, Islamic Commercial Banks are more efficient than Conventional Commercial Banks. As well as using VRS, Islamic Commercial Banks are more efficient then Conventional Commercial Banks.
Keywords: Data Envelopment Anlysis (DEA), Islamic Commercial Bank (BUS), Conventional Commercial Bank (BUK), Constant Return to Scale, Variable Return to Scale, Efficiency.
Scope of the Article: Data Mining and Warehousing