Strategy of Indonesian Navy Big Data Development Using SWOT Analysis and Analytic Hierarchy Process

Authors

  • Bambang Suharjo Sekolah Tinggi Teknologi Angkatan Laut, Indonesia
  • Aria Cakra Wibawa Indonesian Navy Information and Data Analysis Services, Indonesia
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DOI:

https://doi.org/10.33557/journalisi.v3i1.112

Keywords:

Indonesian Navy, Strategy, Big Data, SWOT, AHP

Abstract

The Indonesian Navy organization has the duties and responsibilities of state sovereignty. In carrying out its duties, very large and large data and real time data include data on ships in all Indonesian waters from satellite and terrestrial AIS, from radar, data from long range cameras, data from other agencies such as weather data, marine accident data, natural disasters data and fishing areas data. The managed data consists of structured, semi-structured and unstructured data. Thus, big data is needed within the Indonesian Navy. Therefore, this study analyzes the strategies that need to be taken to be able to develop a big data in the Indonesian Navy. The method used is qualitative analytic research using SWOT and AHP. The research subjects were Section Heads at command and control center, Head of Section at Information and Data Processing Services. Data With this combined method, the resulting main strategy is to implement Big Data with existing and adequate system resources, manageable costs, set up network infrastructure and sufficient bandwidth using available big data software by optimizing government regulations regarding single data for communication and data sharing from other agencies and optimizing advanced API technology.

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Author Biography

  • Aria Cakra Wibawa, Indonesian Navy Information and Data Analysis Services

    Head of Indonesian Navy Information and Data Analysis Services

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Published

2021-03-17

Issue

Vol. 3 No. 1 (2021): Journal of Information Systems and Informatics

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Articles

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How to Cite

[1]
B. Suharjo and A. C. Wibawa, “Strategy of Indonesian Navy Big Data Development Using SWOT Analysis and Analytic Hierarchy Process”, journalisi, vol. 3, no. 1, pp. 201–212, Mar. 2021, doi: 10.33557/journalisi.v3i1.112.

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