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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science
Jonathan, Filbert
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Computer Science & IT

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Investment Modeling Algorithm for the 50 MW Sumbawa 3 Gas Engine Power Plant (PLTMG) Project: A Correlation-Aware, Risk-Adjusted Feasibility Framework Jonathan, Filbert; Damayanti, Sylviana Maya
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2849

Abstract

The 50 MW PLTMG Sumbawa 3 project is included in the RUPTL 2025–2034 to strengthen the Tambora isolated subsystem, whose reserve margin declined to 0.8% in 2024 and 1.4% in 2025. PT PLN (Persero) currently evaluates investments using the Kelayakan Keuangan (KKF) framework, a deterministic Discounted Cash Flow (DCF) model that ignores uncertainty and interdependence among key cost drivers. This study develops a four-layer investment evaluation framework combining strategic analysis (PESTEL, Porter’s Five Forces, SWOT, VRIO), deterministic DCF, sensitivity analysis, and Monte Carlo simulation with 10,000 iterations. Four stochastic variables, LNG price, IDR/USD exchange rate, HSD price, and fixed O&M cost, were modeled under independent and correlated assumptions. The deterministic evaluation indicates feasibility, with an NPV of IDR 276.85 billion, IRR of 9.13%, BCR of 1.03, and a 10.14-year payback period. However, Monte Carlo simulation reveals an 82.08% probability of negative NPV, with a mean NPV of negative IDR 1.42 trillion. The exchange rate and LNG price explain over 99% of the variance in NPV. Results support conditional project approval through LNG price hedging, tariff adjustment mechanisms, accelerated gasification, and mandatory correlation-aware Monte Carlo analysis for future LNG-based generation investments.
Co-Authors Sylviana Maya Damayanti