Peran Matriks Input-Output Dalam Perencanaan Ekonomi Sektoral

Authors

  • Kuswowo Institut Teknologi PLN
  • Mufti Hasan Institut Teknologi PLN
  • Cresencia T. E Siagian Institut Teknologi PLN
  • Tara Syauqina Institut Teknologi PLN

DOI:

https://doi.org/10.36312/jar.v4iSpecial%20Issue.3337

Keywords:

matriks input-output, Keterkaitan Sektoral, Backward Linkage, Forward Linkage, Perencanaan Ekonomi sektoral

Abstract

Matriks input-output merupakan alat analisis ekonomi yang penting untuk memahami struktur dan keterkaitan antar sektor dalam suatu perekonomian secara menyeluruh. Penelitian ini bertujuan untuk menganalisis peran strategis matriks input-output dalam mendukung perencanaan ekonomi sektoral yang berbasis data dan evidence-based policy. Metode penelitian yang digunakan adalah analisis deskriptif kualitatif dengan pendekatan kuantitatif melalui pemanfaatan Tabel Input-Output Nasional yang diterbitkan oleh Badan Pusat Statistik (BPS). Analisis dilakukan dengan menghitung dua indikator utama, yaitu indeks keterkaitan ke belakang (Backward Linkage) dan keterkaitan ke depan (Forward Linkage), guna mengidentifikasi sektor-sektor yang memiliki pengaruh besar dalam mendorong pertumbuhan ekonomi nasional. Hasil penelitian menunjukkan bahwa sektor industri pengolahan, pertanian, dan perdagangan memiliki nilai keterkaitan yang tinggi, baik secara langsung maupun tidak langsung, terhadap sektor-sektor lainnya. Kondisi ini menjadikan ketiga sektor tersebut sebagai sektor unggulan yang berperan strategis dalam struktur perekonomian Indonesia. Temuan ini menegaskan bahwa matriks input-output merupakan instrumen yang relevan dan komprehensif dalam menetapkan prioritas pembangunan sektoral serta mendukung penyusunan kebijakan fiskal dan pembangunan yang lebih terarah. Oleh karena itu, integrasi analisis input-output dalam proses perencanaan ekonomi nasional menjadi krusial untuk meningkatkan efisiensi pengalokasian sumber daya dan memperkuat ketahanan ekonomi nasional.

The input-output matrix is ??an important economic analysis tool for understanding the structure and interrelationships between sectors within an economy as a whole. This study aims to analyze the strategic role of the input-output matrix in supporting data-driven sectoral economic planning and evidence-based policy. The research method used is qualitative descriptive analysis with a quantitative approach through the utilization of the National Input-Output Table published by the Central Statistics Agency (BPS). The analysis is conducted by calculating two main indicators, namely the Backward Linkage index and Forward Linkage, to identify sectors that have a significant influence in driving national economic growth. The results show that the manufacturing industry, agriculture, and trade sectors have high linkages, both directly and indirectly, with other sectors. This condition makes these three sectors as leading sectors that play a strategic role in the structure of the Indonesian economy. These findings confirm that the input-output matrix is ??a relevant and comprehensive instrument in determining sectoral development priorities and supporting the formulation of more targeted fiscal and development policies. Therefore, the integration of input-output analysis in the national economic planning process is crucial to increase the efficiency of resource allocation and strengthen national economic resilience.

References

Aroche Reyes, F., & Marquez Mendoza, M. A. (2021). Demand-driven and supply-sided input–output models. Journal of Quantitative Economics. https://doi.org/10.1007/s40953-020-00229-5

Badan Pusat Statistik. (2020). Tabel input-output Indonesia 2020. Jakarta: BPS RI.

Badan Pusat Statistik. (2021). Produk domestik bruto Indonesia menurut lapangan usaha 2016–2021. Jakarta: BPS RI.

Badan Pusat Statistik. (2023). Indikator ekonomi Indonesia: Statistik sektoral nasional. Jakarta: BPS RI.

Bureau of Economic Analysis. (2023). Regional input–output modeling system (RIMS II). U.S. Department of Commerce. https://www.bea.gov/resources/methodologies/RIMSII

Bzhalava, L., Kaivo-oja, J., & Hassan, S. S. (2018). Data-based Startup Profile Analysis in the European Smart Specialization Strategy: A Text Mining Approach. European Integration Studies, 12, 118–128. https://doi.org/10.5755/j01.eis.0.12.21869

De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061

Dehnokhalaji, A., Ghiyasi, M., & Korhonen, P. (2017). Resource allocation based on cost efficiency. Journal of the Operational Research Society, 68(10), 1279–1289. https://doi.org/10.1057/s41274-016-0020-7

Haghparast-Bidgoli, H., Kiadaliri, A. A., & Skordis-Worrall, J. (2014). Do economic evaluation studies inform effective healthcare resource allocation in Iran? A critical review of the literature. Cost Effectiveness and Resource Allocation, 12(1), 15. https://doi.org/10.1186/1478-7547-12-15

Harmadi, S. H. B. (n.d.). Regional Inequality in Indonesia: Pre and Post Regional Autonomy Analysis.

Imansyah, M. H. (2005). Estimation of regional input-output tables using the hybrid approach: The case of Indonesia. International Input-Output Association (IIOA) Conference Paper. https://www.iioa.org/conferences/13th/files/Imansyah.pdf

Leontief, W. (1936). Quantitative input-output relations in the economic system of the United States. The Review of Economic Statistics, 18(3), 105–125. https://doi.org/10.2307/1927837

Kataoka, M. (2018). Inequality convergence in inefficiency and interprovincial income inequality in Indonesia for 1990–2010. Asia-Pacific Journal of Regional Science, 2(2), 297–313. https://doi.org/10.1007/s41685-017-0051-3

Lim, C., Kim, K.-H., Kim, M.-J., Heo, J.-Y., Kim, K.-J., & Maglio, P. P. (2018). From data to value: A nine-factor framework for data-based value creation in information-intensive services. International Journal of Information Management, 39, 121–135. https://doi.org/10.1016/j.ijinfomgt.2017.12.007

Tadjoeddin, M. Z. (2019). Inequality and Exclusion in Indonesia: Political Economic Developments in the Post-Soeharto Era. Journal of Southeast Asian Economies, 36(3), 284–303.

Yin, S., Li, X., Gao, H., & Kaynak, O. (2015). Data-Based Techniques Focused on Modern Industry: An Overview. IEEE Transactions on Industrial Electronics, 62(1), 657–667. https://doi.org/10.1109/TIE.2014.2308133

Messakh, T. A., Rustiadi, E., Putri, E. I. K., & Fauzi, A. (2021). Dampak sektor transportasi terhadap perekonomian di Timor Barat: Suatu analisis model input-output (IO). Jurnal Wilayah dan Lingkungan, 9(2), 127–141. https://doi.org/10.14710/jwl.9.2.127-141

Mi, Z., Meng, J., Zheng, H., Shan, Y., Wei, Y. M., & Guan, D. (2018). A multi-regional input–output table mapping China’s economic outputs and interdependencies in 2012. Scientific Data, 5, 180155. https://doi.org/10.1038/sdata.2018.155

OECD. (2024). Inter-country input-output (ICIO) tables. Organisation for Economic Co-operation and Development. https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html

Rohmatulloh, B. (2024). Peran matematika dalam analisis ekonomi: Perspektif input-output. Jurnal Ekonomi dan Matematika, 12(1), 45–58. https://doi.org/10.xxxx/jem.2024.12.1.45

Downloads

Published

2025-09-10

How to Cite

Kuswowo, Hasan, M., T. E Siagian, C., & Syauqina, T. (2025). Peran Matriks Input-Output Dalam Perencanaan Ekonomi Sektoral . Journal of Authentic Research, 4(Special Issue), 870-878. https://doi.org/10.36312/jar.v4iSpecial Issue.3337