ANALISIS EFISIENSI OPERASIONAL MASKAPAI PENERBANGAN DOMESTIK DI INDONESIA PERIODE 2019-2023 DENGAN PENDEKATAN DATA ENVELOPMENT ANALYSIS (DEA)
DOI:
https://doi.org/10.54199/11ycqd37Keywords:
Efficiency, Data Envelopment Analysis (DEA), Variabel Return to Scale (VRS), AirlinesAbstract
The air transportation industry plays a strategic role in supporting Indonesia's economic growth, particularly in facilitating passenger mobility, cargo distribution, and regional connectivity. Given its crucial role, a comprehensive analysis is necessary to evaluate the operational efficiency of domestic airlines. This study analyzes the operational efficiency of Indonesian domestic airlines from 2019 to 2023 using the Data Envelopment Analysis (DEA) method with a Variable Return to Scale (VRS) assumption and output orientation. The results indicate that airline efficiency varied throughout the period, with low-cost carriers (LCC) showing more fluctuating efficiency levels compared to full-service carriers. This study recommends that airlines enhance operational flexibility, diversify revenue sources, and invest in data-driven technologies to support more effective decision-making. Furthermore, regulators are encouraged to support airline innovation efforts to improve operational efficiency and strengthen the competitiveness of Indonesia's domestic aviation industry.
References
Aydın, U., Karadayi, M. A., & Ülengin, F. (2020). How efficient airways act as role models and in what dimensions? A superefficiency DEA model enhanced by social network analysis. Journal of Air Transport Management, 82. https://doi.org/10.1016/j.jairtraman.2019.101725
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Ceha, R., & Ohta, H. (2000). Theory and Methodology Productivity change model in the airline industry: A parametric approach. European Journal of Operational Research, 121, 641–655. www.elsevier.com/locate/orms
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Duygun, M., Prior, D., Shaban, M., & Tortosa-Ausina, E. (2016). Disentangling the European airlines efficiency puzzle: A network data envelopment analysis approach. Omega (United Kingdom), 60, 2–14. https://doi.org/10.1016/j.omega.2015.06.004
Eren, M., & Dogan, M. A. (2023). Measurement of Process-Performances of Turkish Airports Using Network Data Envelopment Analysis. Journal of Aviation, 7(2), 272–283. https://doi.org/10.30518/jav.1296416
Farrell, M.J. (1957), The Measurement of Productive Efficiency. Journal of the Royal Statistical Society: Series A (General), 120: 253-281. https://doi.org/10.2307/2343100
Firdaus, M. F., & Hosen, M. N. (2014). Efficiency of Islamic Banks Using Two Stage Approach of Data Envelopment Analysis. Buletin Ekonomi Moneter Dan Perbankan, 16(2), 155–176. https://doi.org/10.21098/bemp.v16i2.442
Ganji, S. S., Mardani, A., & Jahed, R. (2024). Assessment of Iranian airlines using network cross-efficiency DEA and the regret theory. Case Studies on Transport Policy, 18, 101266. https://doi.org/10.1016/j.cstp.2024.101266
Khan, I. U., & Karam, F. W. (2019). Intelligent business analytics using proposed input/output oriented data envelopment analysis DEA and slack based DEA models for US-airlines. Journal of Intelligent and Fuzzy Systems, 37(6), 8207–8217. https://doi.org/10.3233/JIFS-190641
Khezrimotlagh, D., & Kaffash, S. (2024). Navigating Efficiency: Insights from One-Stage and Two-Stage DEA Modeling in the Airline Industry. Annals of Operations Research. https://doi.org/10.1007/s10479-024-06142-x
Losa, E. T., Arjomandi, A., Hervé Dakpo, K., & Bloomfield, J. (2020). Efficiency comparison of airline groups in Annex 1 and non-Annex 1 countries: A dynamic network DEA approach. Transport Policy, 99, 163–174. https://doi.org/10.1016/j.tranpol.2020.08.013
Nurcahyo, Y. B., Ramadhan, G. R., Supriadi, A., Ardiani, G. T., Hastuti, D. L., Siliwangi, U., Tasikmalaya, K., & Barat, J. (2023). PENDEKATAN NON PARAMETRIK: APAKAH INDUSTRI PENERBANGAN INDONESIA SUDAH EFISIEN? WELFARE Jurnal Ilmu Ekonomi, 4(1). http://jurnal.unsil.ac.id/index.php/welfare
Omrani, H., Fahimi, P., & Emrouznejad, A. (2022). A common weight credibility data envelopment analysis model for evaluating decision making units with an application in airline performance. RAIRO - Operations Research, 56(2), 911–930. https://doi.org/10.1051/ro/2022031
Queiroz Júnior, H. da S., Celestino, M. A. de S., de Souza, C. F. F., Falcão, V. A., & Camioto, F. de C. (2024). CO2 Emissions in Air Transport: A Comparative Study Using Data Envelopment Analysis. Transportation Research Record, 2678(5), 872–883. https://doi.org/10.1177/03611981231193407
Rusydiana, A. S., & Consulting, T. S. (2013). Mengukur Tingkat Efisiensi dengan Data Envelopment Analysis. Bogor: SMART Publishing.
Saini, A., Truong, D., & Pan, J. Y. (2023). Airline efficiency and environmental impacts – Data envelopment analysis. International Journal of Transportation Science and Technology, 12(2), 335–353. https://doi.org/10.1016/j.ijtst.2022.02.005
Scheraga, C. A. (2004). Operational efficiency versus financial mobility in the global airline industry: A data envelopment and Tobit analysis.
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