APPLICATION OF NATURAL LANGUAGE PROCESSING TO ENHANCE PLANNED MAINTENANCE EFFECTIVENESS ON INDONESIAN PIONEER SHIPS
Keywords:
Maritime Digital Transformation, Natural Language Processing, Pioneer Ships, Planned Maintenance, Predictive MaintenanceAbstract
This research investigates the application of Natural Language Processing (NLP) technology to enhance planned maintenance effectiveness on Indonesian pioneer ships. Pioneer vessels play a crucial role in connecting remote archipelagic regions, yet their maintenance systems remain largely traditional and reactive. Through qualitative analysis involving maintenance personnel, ship operators, and maritime technical experts, this study explores how NLP can transform maintenance documentation processing, failure prediction, and decision-making support. Results indicate that NLP-based systems can significantly improve maintenance scheduling accuracy, reduce unplanned downtime, and optimize resource allocation. The research identifies key implementation challenges including data quality, linguistic complexity of maintenance documentation, and integration with existing systems. Findings demonstrate that contextual adaptation of NLP technologies to Indonesian maritime operations can achieve substantial operational efficiency improvements while supporting fleet modernization objectives. This study contributes to maritime digital transformation literature by providing evidence-based frameworks for AI-driven maintenance management in developing maritime contexts, offering practical pathways for technological adoption in resource-constrained environments
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Copyright (c) 2026 Aldo Deviano, Ardiansyah Ardiansyah, Natanael Suranta, Pesta Veri A. N., Yusuf Pria Utama, Chanra Purnama (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
