Artificial Intelligence Integration in Naval Material Maintenance: A Qualitative NVivo and SWOT Analysis of SIGMA-Class Corvette

Sugiharto; Ahmad Faisol; Tony Priyo Utomo; Zainubbi

doi:10.55927/ijis.v5i9.69

Authors

Sugiharto Indonesian Naval Command and Staff College (Seskoal)
Ahmad Faisol Indonesian Naval Command and Staff College (Seskoal)
Tony Priyo Utomo Indonesian Naval Command and Staff College (Seskoal)
Zainubbi Indonesian Naval Command and Staff College (Seskoal)

DOI:

https://doi.org/10.55927/ijis.v5i9.69

Keywords:

Artificial Intelligence, Predictive Maintenance, SIGMA-Class Corvettes, Diesel Generator, NVivo 15, SWOT Analysis, Systems Engineering

Abstract

Integrating Artificial Intelligence (AI) into naval material maintenance represents a vital strategic imperative to enhance technical readiness and operational reliability across warship fleets. This research examines the implementation of AI-based early fault detection systems for Caterpillar AR3406C diesel generators on SIGMA-class corvettes within Satkor Koarmada II. Utilizing a qualitative descriptive methodology, the study integrates in-depth interviews with key naval leadership and technical stakeholders, direct engine room observations, policy document analysis, and qualitative thematic coding via NVivo 15 software combined with IFAS-EFAS SWOT matrices, Reliability-Centered Maintenance, and the Technology Acceptance Model. The empirical findings identify critical technological bottlenecks, establish an AI-driven predictive maintenance architecture, and formulate a structured strategic implementation roadmap. This study confirms that AI-based predictive maintenance serves as a vital cornerstone for modern naval material logistics, minimizing downtime and effectively safeguarding national maritime sovereignty.

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