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Analisis Kelayakan Investasi Proyek EPC Pengembangan Coal Handling Facility (CHF) Rail Loop Serta Train Loading System (TLS) 6 Dan 7 Aditya Wiyanto; Budi Santosa
JIBEMA: Jurnal Ilmu Bisnis, Ekonomi, Manajemen, dan Akuntansi Vol. 4 No. 1 (2026): July
Publisher : CV. Muris Global Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62421/jibema.v4i1.599

Abstract

Penelitian ini melakukan analisis risiko menggunakan dua pendekatan, yaitu kerangka deterministik berbasis skenario (optimistis, moderat, dan pesimistis) serta pendekatan simulasi Monte Carlo probabilistik untuk mengkuantifikasi distribusi nilai NPV dan IRR yang mungkin, dengan mensimulasikan variasi simultan faktor masukan. Arus kas dibangun berbasis manfaat incremental proyek (net incremental benefit) setelah pajak, bukan total revenue/OPEX korporat, dengan komponen CAPEX, OPEX, pajak, dan terminal value dinyatakan secara eksplisit. Hasil penelitian menunjukkan bahwa proyek ini layak secara teknis dan finansial pada kondisi dasar, sebagaimana ditunjukkan oleh nilai NPV yang positif, IRR lebih besar daripada WACC, dan BCR lebih dari 1. Simulasi Monte Carlo dijalankan sebanyak 10.000 iterasi dan menunjukkan bahwa proyek ini berisiko, tetapi tidak berlebihan, dengan probabilitas pemenuhan kriteria kelayakan berada pada kisaran 58%-64% serta sebaran nilai NPV, IRR, BCR, dan PP yang cukup lebar. Oleh karena itu, strategi manajemen risiko yang kuat dan operasional harus diterapkan untuk menjaga investasi tetap stabil dan memungkinkan proyek untuk berkembang secara berkelanjutan.
Fuzzy spatial assignment and NSGA-II approach to multi-objective covering problems in substation expansion planning Eduard Nugroho Theopilus; Budi Santosa
International Journal of Industrial Optimization Vol. 7 No. 2 (2026) [IN PRESS]
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v7i2.14108

Abstract

This study addresses a large-scale Multi-Objective Covering Problem (MOCP) in urban electricity distribution, involving 416.472 customers and 732 substations. The main objective is to optimize the allocation of customers to substations, taking into account trade-offs between three objectives: minimizing average service distance, balancing load, and minimizing substation investment costs. To achieve this, we propose a hybrid framework that combines a constrained fuzzy C-means algorithm for customer assignment, NSGA-II algorithm for multi-objective optimization, and K-means clustering for facility expansion. The results show a trade-off between the objectives, with the composite evaluation identifying k=7 as the best compromise. Statistical validation confirms the significance of these results. Significantly, the proposed framework can generate scalable solutions for MOCP in the real world. By integrating construction cost data and spatial data, the model shows a 15% reduction in substation investment costs and an 8% increase in service reliability. This research provides practical insights for distribution network planners, by offering a data-driven approach to determine the optimal number and strategic placement of additional substations, considering trade-offs between cost, performance, and spatial constraints.