Peran Matematika Ekonomi dalam Pengambilan Keputusan Ekonomi

Anisa Dinda Pertiwi; Clarita Joyce Sitanggang; Dina Larisma Hotmauli Lumban Gaol; Putri Uli Sitompul; Wafiq Mukhlis Rifat

doi:10.69693/ijmst.v4i3.8184

Authors

Anisa Dinda Pertiwi Universitas Negeri Medan
Universitas Ngerei medan Universitas Negeri Medan
Dina Larisma Hotmauli Lumban Gaol Universitas Negeri Medan
Universitas Ngerei medan Universitas Negeri Medan
Wafiq Mukhlis Rifat Universitas Negri Medan

DOI:

https://doi.org/10.69693/ijmst.v4i3.8184

Keywords:

Matematika Ekonomi, Keputusan Ekonomi, Produksi, Harga, Biaya, Optimasi

Abstract

Matematika ekonomi merupakan pendekatan kuantitatif yang digunakan untuk merepresentasikan hubungan antarvariabel ekonomi melalui fungsi, persamaan, turunan, dan teknik optimasi. Penelitian ini bertujuan menjelaskan peran matematika ekonomi dalam membantu pengambilan keputusan ekonomi, terutama pada analisis biaya, penentuan harga, perencanaan produksi, dan pemilihan alternatif keputusan yang efisien. Penelitian menggunakan metode kualitatif dengan pendekatan studi literatur dengan menelaah buku, artikel ilmiah, jurnal, dan sumber informasi relevan yang membahas penerapan model matematis dalam kegiatan ekonomi. Hasil kajian menunjukkan bahwa fungsi biaya dapat digunakan untuk menghitung biaya tetap, biaya variabel, biaya total, biaya rata-rata, dan biaya marginal sehingga pelaku usaha dapat menilai efisiensi produksi. Pada aspek harga, fungsi permintaan dan penawaran serta konsep elastisitas membantu menentukan harga keseimbangan dan memperkirakan respons konsumen terhadap perubahan harga. Dalam perencanaan produksi, turunan dan optimasi membantu menentukan tingkat output yang memberikan keuntungan maksimum atau biaya minimum. Contoh perhitungan fungsi linear juga menunjukkan bahwa hubungan antara pendapatan, biaya, dan target keuntungan dapat dinyatakan secara sistematis untuk menentukan jumlah unit yang harus dijual. Secara keseluruhan, matematika ekonomi memberikan dasar analisis yang rasional, objektif, terukur, dan dapat diuji sehingga keputusan ekonomi tidak hanya bergantung pada intuisi. Penerapan model matematis juga membantu individu dan perusahaan menggunakan sumber daya terbatas secara lebih efisien, mengevaluasi berbagai alternatif, serta mengurangi risiko kesalahan dalam pengambilan keputusan.

References

1. Hauck, Z., Rabta, B., & Reiner, G. (2021). Joint quality and pricing decisions in lot sizing models with defective items. International Journal of Production Economics, 241, 108255. https://doi.org/10.1016/j.ijpe.2021.108255

2. Feng, L., Wang, W.-C., Teng, J.-T., & Cárdenas-Barrón, L. E. (2022). Pricing and lot-sizing decision for fresh goods when demand depends on unit price, displaying stocks and product age under generalized payments. European Journal of Operational Research, 296(3), 940–952. https://doi.org/10.1016/j.ejor.2021.04.023

3. Gurkan, M. E., Tunc, H., & Tarim, S. A. (2022). The joint stochastic lot sizing and pricing problem. Omega, 108, 102577. https://doi.org/10.1016/j.omega.2021.102577

4. Liu, M., Tang, H., Chu, F., Zheng, F., & Chu, C. (2022). Joint optimization of lot-sizing and pricing with backlogging. Computers & Industrial Engineering, 167, 107979. https://doi.org/10.1016/j.cie.2022.107979

5. Terzi, M., Ouazene, Y., Yalaoui, A., & Yalaoui, F. (2022). Integrated lot-sizing and pricing problem under cross-price demand model. IFAC-PapersOnLine, 55(10), 2372–2377.

6. Chu, H., Dong, K., Li, R., Cheng, Q., Zhang, C., Huang, K., Yang, C., & Zheng, Y. (2022). Integrated modeling and optimization of production planning and scheduling in hybrid flow shop for order production mode. Computers & Industrial Engineering, 174, 108741. https://doi.org/10.1016/j.cie.2022.108741

7. Gahler, D., & Hruschka, H. (2023). Heuristic pricing rules not requiring knowledge of the price response function. Review of Managerial Science, 17, 2325–2347. https://doi.org/10.1007/s11846-022-00581-1

8. Terzi, M., Ouazene, Y., Yalaoui, A., & Yalaoui, F. (2023). Lot-sizing and pricing decisions under attraction demand models and multi-channel environment: New efficient formulations. Operations Research Perspectives, 10, 100269. https://doi.org/10.1016/j.orp.2023.100269

9. Gentile, C., Pinto, D. M., & Stecca, G. (2023). Price of robustness optimization through demand forecasting with an application to waste management. Soft Computing, 27, 13013–13024. https://doi.org/10.1007/s00500-022-07148-y

10. Ćwik, K., Skrzypek-Ahmed, S., Cwynar, W., Skowron, S., & Mróz-Gorgoń, B. (2023). Optimisation models and the economics of managerial decision-making. Journal of Modern Science, 54(5), 810–825. https://doi.org/10.13166/jms/176485

11. Malasari, T., Pratama, A., Naibaho, A., Fransiska, C., & Zefanya, K. (2023). Analisis penerapan matematika dalam ilmu ekonomi: Fungsi permintaan dan penawaran. Innovative: Journal of Social Science Research, 3(2), 5557–5565.

12. Hsieh, C.-L., Tsai, W.-H., & Chu, P.-Y. (2024). Analyzing production optimization models across various carbon pricing policies. International Journal of Production Research, 62(18), 6713–6731. https://doi.org/10.1080/00207543.2023.2296971

13. Wang, K.-J., & Simarmata, M. T. A. (2024). Fuzzy activity-based costing: An investment evaluation approach for management information system of a smart factory. The Engineering Economist, 69(1), 66–86. https://doi.org/10.1080/0013791X.2024.2314673

14. Feng, Y., & Liu, S. (2024). Research on price formation based on resource optimization allocation. Sustainability, 16(12), 5129. https://doi.org/10.3390/su16125129

15. Petropoulos, T., Patsis, P., Liapis, K., & Chytis, E. (2024). A double optimum new solution method based on EVA and knapsack. Journal of Risk and Financial Management, 17(11), 498. https://doi.org/10.3390/jrfm17110498

16. Kobiyh, M., & El Amri, A. (2024). Rational individual and managerial decision model: A critical review of the standard rationality hypothesis. Business Ethics and Leadership, 8(3), 120–132. https://doi.org/10.61093/bel.8(3).120-132.2024

17. Oktaviani, L., & Tambunan, N. (2024). Pengaruh pentingnya pembelajaran matematika ekonomi terhadap pengelolaan keuangan pribadi siswa. Jurnal Ilmiah Wahana Pendidikan, 10(16), 488–495. https://doi.org/10.5281/zenodo.13764155

18. Terzi, M., Ouazene, Y., Yalaoui, A., & Yalaoui, F. (2024). Matheuristics vs. metaheuristics for joint lot-sizing and dynamic pricing problem with nonlinear demands. Computers & Operations Research, 163, 106507. https://doi.org/10.1016/j.cor.2023.106507

19. Hidayanti, N. F., Syaharuddin, & Ariani, Z. (2025). Product price optimization in microeconomics: Exploring the role of artificial intelligence algorithms – A literature review. Jurnal Al-Azhar Indonesia Seri Sains dan Teknologi, 10(2), 171–177. https://doi.org/10.36722/sst.v10i2.3770

20. Mansur, R. R., Yeremia, Gitama, G. N. D. P., & Dewi, W. N. (2025). Pentingnya pengetahuan matematika ekonomi sebagai konsep dasar berbisnis menggunakan fungsi biaya. Jurnal Witana, 3(2), 59–64.

21. Saputra, H., Zakiah, N., & Happy, N. (2025). Optimization of pricing and cost planning using linear functions: An economic mathematics perspective on financial strategy at Cake My Day. International Journal of Economic Literature, 3(2), 625–635.

22. Chiang, A. C., & Wainwright, K. (2005). Fundamental Methods of Mathematical Economics (4th ed.). McGraw-Hill.

23. Mankiw, N. G. (2019). Principles of Economics (8th ed.). Cengage Learning.

24. Marshall, A. (1890). Principles of Economics. Macmillan.

25. Nicholson, W., & Snyder, C. (2012). Microeconomic Theory: Basic Principles and Extensions (11th ed.). South-Western Cengage Learning.

26. Samuelson, P. A. (1947). Foundations of Economic Analysis. Harvard University Press.

27. Simon, H. A. (1978). Rational Decision Making in Business Organizations. Nobel Prize Lecture.

28. Varian, H. R. (2014). Intermediate Microeconomics: A Modern Approach (9th ed.). W. W. Norton & Company.