Analisis Efisiensi Teknis Infrastruktur Jalan Kabupaten dan Kota di Indonesia Menggunakan Metode Data Envelopment Analysis (DEA)
Main Article Content
Abstract
Penelitian ini bertujuan menganalisis efisiensi pengelolaan infrastruktur jalan kabupaten/kota di Indonesia dengan menerapkan metode Data Envelopment Analysis (DEA) model Variable Returns to Scale (VRS). Analisis dilakukan untuk periode 2023–2024 dengan menggunakan variabel input berupa total panjang jalan dan panjang jalan tidak mantap, serta output berupa panjang jalan mantap. Hasil penelitian menunjukkan bahwa efisiensi antarprovinsi bervariasi, terutama dalam kemampuan mengonversi jaringan jalan yang dimiliki menjadi jalan mantap. Perbandingan skor DEA dua tahun menunjukkan bahwa posisi relatif provinsi terhadap frontier berubah secara dinamis, dengan sebagian daerah mengalami peningkatan efisiensi dan sebagian lainnya mengalami penurunan. Rata-rata efisiensi nasional berada pada angka sekitar 0,79 dari 37 provinsi di Indonesia, yang mengindikasikan bahwa pemanfaatan sumber daya dalam pengelolaan jaringan jalan masih belum optimal di banyak wilayah. Penelitian ini dapat mengetahui faktor penyebab dan mekanisme kebijakan yang sinergis antara kabupaten dan kota. Implikasi penelitian ini secara praktis dapat menegaskan perlunya peningkatan kinerja teknis dan perencanaan berbasis efisiensi untuk mendorong peningkatan proporsi jalan mantap di Indonesia.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[1] R. Indah et al., “The interplay of road infrastructure and regional finance in driving economic growth: Insights from East Kalimantan,” J. Open Innov. Technol. Mark. Complex, vol. 11, no. 1, p. 100444, 2025, doi: 10.1016/j.joitmc.2024.100444.
[2] “Kementerian PUPR Percepat Pelaksanaan Inpres Jalan Daerah 2025,” 2024, Kementerian Pekerjaan Umum dan Perumahan Rakyat (PUPR) Republik Indonesia. [Online]. Available: https://sahabat.pu.go.id/eppid/berita/detail/kementerian-pu-percepat-pelaksanaan-inpres-jalan-daerah-2025
[3] S. Hartono, A. F. Rahman, and M. Tojibussabirin, “Determinants of Infrastructure Spending Efficiency in Indonesia: Data Envelopment Analysis (DEA) and Tobit Regression Approach,” J. World Sci., vol. 2, no. 8, pp. 1249–1256, 2023, doi: 10.58344/jws.v2i8.347.
[4] M. R. Wisnu Aji and E. Letizia, “Assesing The Efficiency Of Central Java’s Regional Budget Realization Using Data Envelopment Analysis,” Equity J. Ekon., vol. 13, no. 1, pp. 113–122, 2025, doi: 10.33019/equity.v13i1.434.
[5] S. Wu, L. Ma, and Y. Chen, “A Comprehensive Overview Based on Data Envelopment Analysis (DEA): An Approach Towards Green Economy Development and Sustainability,” 2025, doi: https://doi.org/10.1007/s11067-025-09696-w.
[6] V. Foster and S. Straub, “The Impact of Infrastructure on Development Outcomes A Qualitative Review of Four Decades of Literature,” no. March, 2023.
[7] K. S. Diallo, P. Mendy, G. Degla, and B. M. Ndiaye, “Data Envelopment Analysis and Bootstrap Approaches for E ffi ciency Measure of the Autonomous Port of Dakar,” vol. 14, no. 4, pp. 51–64, 2022.
[8] J. Santos, A. Ferreira, and G. Flintsch, “An adaptive hybrid genetic algorithm for pavement management,” Int. J. Pavement Eng., vol. 20, no. 3, pp. 266–286, 2019.
[9] S. Ahangari, C. Chavis, M. Jeihani, and Z. R. Moghaddam, “Quantifying the effect of on-street parking information on congestion mitigation using a driving simulator,” Transp. Res. Rec., vol. 2672, no. 8, pp. 920–929, 2018.
[10] T. Han, R. Ma, and J. Zheng, “Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis,” Measurement, vol. 176, no. January, p. 109208, 2021, doi: 10.1016/j.measurement.2021.109208.
[11] A. Mergoni, A. Emrouznejad, and K. De Witte, “Fifty years of Data Envelopment Analysis,” Eur. J. Oper. Res., vol. 326, no. 3, pp. 389–412, 2025, doi: 10.1016/j.ejor.2024.12.049.
[12] A. Emrouznejad and G. Yang, “A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016,” Socioecon. Plann. Sci., vol. 61, pp. 4–8, 2018.
[13] C. J. O’Donnell, “Econometric estimation of distance functions and associated measures of productivity and efficiency change,” J. Product. Anal., vol. 41, no. 2, pp. 187–200, 2014.
[14] T. Ten Raa, Microeconomics: equilibrium and efficiency. Bloomsbury Publishing, 2017.
[15] A. Emrouznejad and G. Yang, “Socio-Economic Planning Sciences Invited paper A survey and analysis of the fi rst 40 years of scholarly literature in DEA : 1978 e 2016,” Socioecon. Plann. Sci., vol. 61, pp. 4–8, 2018, doi: 10.1016/j.seps.2017.01.008.
[16] J. Zhu, Data envelopment analysis: A handbook of empirical studies and applications, vol. 238. Springer, 2016.
[17] X. Ji, J. Wu, Q. Zhu, and J. Sun, “Using a hybrid heterogeneous DEA method to benchmark China’s sustainable urbanization: An empirical study,” Ann. Oper. Res., vol. 278, no. 1, pp. 281–335, 2019.