XAI-Powered Intelligent Compliance Management Systems for STCW and Environmental Regulatory Documentation at Maritime Training Institutions

Authors

  • Natanael Suranta Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Bambang Kurniadi Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Titis Ari Wibowo Sekolah Tinggi Ilmu Pelayaran Jakarta

DOI:

https://doi.org/10.55123/ijisit.v3i1.59

Keywords:

Explainable Artificial Intelligence, Regulatory Compliance, STCW Certification, Maritime Education Quality Assurance, Knowledge Graphs

Abstract

Maritime training institutions face overwhelming regulatory compliance burdens consuming 2,400+ annual staff hours manually compiling STCW certification evidence, verifying instructor qualifications against 2,847 specific requirements, cross-referencing simulator logs with competency standards, and preparing documentation for Port State Control inspections, yet manual processes suffer from 12-15% error rates creating certificate validity challenges, regulatory audit deficiencies, and graduate employability risks when documentation inadequacies discovered during vessel inspections. This research presents the design and validation of explainable artificial intelligence systems automating regulatory compliance verification while maintaining transparency and human oversight through interpretable decision logic, natural language explanations, and audit trail generation at Sekolah Tinggi Ilmu Pelayaran Jakarta. Employing design science research methodology with qualitative stakeholder evaluation, the study engaged compliance officers (n=8), quality assurance specialists (n=6), and regulatory auditors (n=7) through structured interviews examining automation accuracy, explanation adequacy, and institutional trust development. The knowledge graph-based XAI architecture integrated STCW Convention requirements, MARPOL environmental regulations, Indonesian maritime law, and institutional policies into unified semantic network enabling automated compliance verification, gap detection, and proactive remediation recommendations with SHAP-based feature attribution and attention mechanism visualization explaining AI reasoning. Thematic analysis revealed strong support for explainable compliance automation, identifying critical themes of administrative efficiency, audit readiness enhancement, and regulatory confidence improvement. Pilot implementation across 12-month period demonstrated 94% compliance verification accuracy, 89% reduction in manual documentation time (2,400 to 264 annual hours), 97% improvement in audit preparation completeness, and zero regulatory deficiencies during Ministry of Transportation accreditation inspection, contributing validated XAI architectures and empirical evidence supporting transparent intelligent automation in maritime regulatory compliance contexts addressing administrative burden reduction and institutional quality assurance imperatives.

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References

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Published

2026-06-15

How to Cite

Natanael Suranta, Kurniadi, B., & Wibowo, T. A. (2026). XAI-Powered Intelligent Compliance Management Systems for STCW and Environmental Regulatory Documentation at Maritime Training Institutions. IJISIT: International Journal of Computer Science and Information Technology, 3(1), 12–23. https://doi.org/10.55123/ijisit.v3i1.59

Issue

Vol. 3 No. 1 (2026): June 2026

Section

Articles

License

Copyright (c) 2026 Natanael Suranta, Bambang Kurniadi, Titis Ari Wibowo

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