Financial Feasibility Study For Entering The New Market Of Ai Traffic Counting Technology
(Case Study Of PT. Jasa Marga Tollroad Operator)
Keywords:
AI traffic counting, feasibility study, financial analysis, JMTO, toll road technology
Abstract
This study evaluates the financial feasibility of PT. Jasa Marga Tollroad Operator (JMTO) for entering a new market with AI-based traffic counting technology. Motivated by increasing traffic congestion and the potential of AI to optimize traffic monitoring, the research applies both strategic and financial analyses. A combination of internal and external assessments, including PESTEL, Porter’s Five Forces, and VRIO frameworks, is used to understand market dynamics and competitive advantages. Financial feasibility is determined using Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Profitability Index (PI), with additional insights from sensitivity and Monte Carlo analyses. The results indicate a positive NPV of Rp 1.57 billion, IRR of 16.06% (higher than the WACC of 13.79%), a PI of 1.165, and a payback period of 3.35 years, confirming financial viability. Sensitivity analysis reveals selling price and operating cost as key risk factors, while Monte Carlo simulations affirm a 0% probability of negative NPV. The findings support JMTO’s strategic move into the AI traffic counting market and recommend proactive cost control, strategic procurement, and collaboration with government stakeholders to mitigate risks and enhance implementation success.References
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Frank, M. Z., & Shen, T. (2016). Investment and the weighted average cost... Journal of Financial Economics, 119(2), 300–315.
Haney, M. (2016). Teaching the concept of investment risk... Journal of the Academy of Business Education, Fall, 236–256.
Herlina, L., Listri, & Nugroho, I. (2024). Assessing the impact of current ratio... Journal of Accounting and Finance, 8(2).
Jovanovic, P. (1999). Application of sensitivity analysis... International Journal of Project Management, 17(4), 217–222.
Odavic, P., et al. (2017). Life cycle cost of biomass power plant... Ekonomika Poljoprivrede, 64(2), 587–599.
Rezzouk, H., & Mellit, A. (2015). Feasibility study and sensitivity analysis... Renewable and Sustainable Energy Reviews, 43, 1134–1150.
Ristama, H. (2023). The utilization of intelligent traffic systems... Deviance: Jurnal Kriminologi, 7(1), 1–10.
Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard Business Review, 86(1), 78–93.
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Clemen, R. T. (1997). Making hard decisions: An introduction to decision analysis. Duxbury Press.
Gitman, L. J., & Zutter, C. J. (2014). Principles of managerial finance (14th ed.). Pearson Education.
Gitman, L. J. (2012). Risk and return. In L. J. Gitman, Principles of managerial finance. Pearson.
Rothaermel, F. T. (2015). Strategic management (2nd ed.). McGraw-Hill Education.
Ross, S. A., Westerfield, R. W., & Jaffe, J. (2016). Corporate finance (11th ed.). McGraw-Hill Education.
Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: A guide to assessing scientific models. John Wiley & Sons.
Suliyanto. (2010). Studi kelayakan bisnis: Pendekatan praktis. Andi.
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Amazon Web Services. (n.d.). What is Monte Carlo simulation? https://aws.amazon.com/id/what-is/montecarlo-simulation/
International Organization for Standardization. (2018). ISO 31000:2018 Risk management – Guidelines. https://www.iso.org/standard/65694.html
Kenton, W. (2019). Monte Carlo simulation. Investopedia. https://www.investopedia.com/terms/m/montecarlosimulation.asp
The Jakarta Daily. (2022, May 15). New record: Number of Eid homecoming vehicles... https://www.jakartadaily.id/local/...
Published
2025-12-21
How to Cite
Nugroho, I. W., & Damayanti , S. M. (2025). Financial Feasibility Study For Entering The New Market Of Ai Traffic Counting Technology . Kontigensi : Jurnal Ilmiah Manajemen, 13(2), 998-1015. https://doi.org/10.56457/jimk.v13i2.803
Section
Articles
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