Optimizing Maritime Domain Awareness through Artificial Intelligence Integration and Personnel Digital Competence: A Qualitative NVivo and SWOT Analysis of Satsurvei Pushidrosal

Senthiko Pinahayu; Dian Tri Hutanto; Dewadharu Achsyan; Achmad Faisol; Rudi Hartono Siregar

doi:10.55927/ijis.v5i9.87

Authors

Senthiko Pinahayu Indonesian Naval Command and Staff College (Seskoal)
Dian Tri Hutanto Indonesian Naval Command and Staff College (Seskoal)
Dewadharu Achsyan Indonesian Naval Command and Staff College (Seskoal)
Achmad Faisol Indonesian Naval Command and Staff College (Seskoal)
Rudi Hartono Siregar STTAL Bumi Moro Surabaya

DOI:

https://doi.org/10.55927/ijis.v5i9.87

Keywords:

Maritime Domain Awareness, Artificial Intelligence, Digital Competence, Hydro-Oceanography, NVivo 12 Plus, SWOT Analysis, Digital Transformation

Abstract

Maritime Domain Awareness (MDA) demands rapid, accurate, integrated maritime information, yet expanding hydro-oceanographic data challenge conventional processing. This qualitative case study examines Artificial Intelligence (AI) use and personnel digital competence at the Survey Unit (Satsurvei) of Indonesia’s Navy Hydro-Oceanographic Center (Pushidrosal), formulating an MDA optimization strategy. Data from five key informants, field observation, and document analysis were coded in NVivo 12 Plus. Internal and external factors were assessed using IFAS–EFAS and SWOT, guided by Digital Transformation Theory. Findings reveal AI adoption remains early, limited to rule-based automation and constrained by infrastructure, interoperability, and data governance. Personnel digital competence is uneven, weakest in AI understanding and interpreting AI output. SWOT places Satsurvei in Quadrant I (IFAS +0.007; EFAS +0.005), supporting an aggressive SO strategy. The integrated strategy combines survey data, personnel experience, and organizational support through a digital transformation roadmap, dedicated budgeting, integrated digital infrastructure, AI analysis modules, systematic AI training, and interagency data sharing. Phased implementation and evaluation are recommended

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