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Journal : malcom indonesian journal of machine learning and computer science

Factors Causing Ineffectiveness of Capex-Opex Management on the Reliability of Coal-Fired Power Plants Fachrudin, Mohammad Anang; Gunarta, I Ketut
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.v6i3.2670

Abstract

This study examines the ineffectiveness of Capital Expenditure (Capex) and Operational Expenditure (Opex) management in improving the reliability of coal-fired power plants in isolated electricity systems, using PLTU Bolok and PLTU Ropa in East Nusa Tenggara as case study units. The study is motivated by fluctuations in Equivalent Availability Factor (EAF) and Equivalent Forced Outage Rate (EFOR) during 2019-2024, despite recurring maintenance and investment expenditures. A descriptive case-study design was applied, combining reliability and maintenance performance analysis with Root Cause Analysis (RCA) using a Fishbone Diagram, Pareto Analysis, and Five Whys. The dataset includes Capex and Opex realization, FOH, POH, downtime, maintenance backlog, and the composition of preventive and corrective maintenance. The findings show that ineffective Capex-Opex management is primarily driven by equipment degradation, maintenance delays, high backlog levels, delays in spare-part procurement, delayed critical investments, and the dominance of corrective maintenance. The novelty of this study lies in integrating cost-allocation evaluation, reliability indicators, and root-cause diagnosis into a single analytical framework for isolated coal-fired power plant systems. In practice, the results imply that reliability improvement should prioritize reliability-based maintenance planning, backlog reduction, control of critical-spare procurement, and Capex prioritization based on asset criticality rather than budget magnitude alone.
Decision-Making on Additional Power Supply for the Timor System: A Systematic Literature Review of Analytic Network Process Applications Mahaprasetya, I Dewa Gde Budhita; Gunarta, I Ketut
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.v6i3.2712

Abstract

This study employs a Systematic Literature Review (SLR) to synthesize empirical findings on the application of the Analytic Network Process (ANP) in energy planning and power-supply decision-making. The review identifies commonly used evaluation criteria, assessed alternatives, and strategic implications for electricity-system planning, particularly in constrained or semi-isolated systems such as the Timor System. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework, including identification, screening, eligibility assessment, and inclusion. Relevant literature was retrieved from Scopus and Google Scholar using keywords related to ANP, power-system planning, energy storage, generation expansion, and electricity decision-making. Of 52 records identified, eight studies satisfied the inclusion, eligibility, and quality-assessment criteria and were synthesized. The findings show that ANP effectively evaluates alternatives involving interdependent technical, operational, economic, environmental, and policy criteria. Battery Energy Storage Systems (BESS) and other flexible low-carbon technologies are generally preferred when reliability, flexibility, and policy alignment are prioritized, whereas conventional thermal generation remains competitive under cost-oriented scenarios. This review transforms dispersed ANP evidence into a structured decision-making framework for the Timor System and recommends evaluating future power-supply alternatives using reliability-oriented, scenario-based, and policy-aligned weighting instead of relying solely on economic comparisons.