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A Conseptual Application of Artificial Neural Networks in the Oil Offloading Process from FPSO to Tanker: Cargo Loss Perspective

Maritime Park: Journal of Maritime Technology and Society · Vol. ume (2026) · pp. 365–373

ISSN
2828-6669, 2828-7010
Published
2026-06-25
Publisher
Department of Ocean Engineering, Faculty of Engineering, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Artificial Intelligence has experienced significant improvements, including its use in sustainable maritime security. Offloading oil from FPSO to tankers can potentially lose cargo due to sabotage from fraudulent parties. Even though international laws and regulations have been enacted, this potential cannot be controlled. Various approaches and researchers have used Artificial Intelligence in the maritime aspect, but they have yet to discuss it from the perspective of cargo loss. The application of intelligent technology in the offloading process aims to create an integrated and coordinated system of parties involved in oil shipping operations using tankers. Therefore, a systematic review of Artificial Intelligence was conducted to find a solution from the perspective of load loss due to sabotage in the oil offloading process. Overall, we find that the potential for oil sabotage always exists in delivering tankers from the FPSO to their destination. The Automatic Integrated System is the Artificial Intelligence most often used to control maritime security. Instead, we see a need for cloud computing, internet-of-things, and big data analytics, which play a critical role in maritime security today.

Keywords

Collaboration network of the authors

38 authors · 62 co-authorship links

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  • Universitas Halu Oleo
  • Muhammadiyah University of Makassar
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fuad Mahfud AssidiqAffiliation unknown2
Ashury AshuryAffiliation unknown2
Daeng ParokaAffiliation unknown2
Windi WidianingrumAffiliation unknown2
Muammar KadhafiAffiliation unknown1
Muh. SyaifullahAffiliation unknown1
Paroka ParokaAffiliation unknown1
Risal RisalAffiliation unknown1
Firman HusainAffiliation unknown1
Cynthia Sampe LoloAffiliation unknown1
Faizal SattuAffiliation unknown1
Sahwan RamadhanAffiliation unknown1
Faulina KhusniawatiAffiliation unknown1
Sapit HidayatAffiliation unknown1
AMAR MA’RUFUniversitas Halu Oleo1
Wahyuddin WahyuddinMuhammadiyah University of Makassar1
Nabila Ainun Nur RahmatAffiliation unknown1
Makbul MubarakAffiliation unknown1
Ahmad Faiq Dhiyaulhaq KaryaAffiliation unknown1
Nurul HaslindahAffiliation unknown1
Ainun JariahAffiliation unknown1
Wazirotus SakinahAffiliation unknown1
Rieke N. SAffiliation unknown1
A. Rifka P. SAffiliation unknown1
Sugeng MarsudiAffiliation unknown1
Amiruddin AmiruddinAffiliation unknown1
Yusman YusmanAffiliation unknown1
Apriansah ApriansahAffiliation unknown1
Nurjurana JusmanAffiliation unknown1
Citra SugengAffiliation unknown1
Andi KhairAffiliation unknown1
Andi Babul KhairAffiliation unknown1
Heriana KhairAffiliation unknown1
Egi SikalaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/maritimepark/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">A Conseptual Application of Artificial Neural  Networks in the Oil Offloading Process from FPSO to  Tanker: Cargo Loss Perspective  </dc:title>
	<dc:creator xml:lang="en">Palippui, Habibi</dc:creator>
	<dc:creator xml:lang="en">Rosyid, Daniel Mohammad</dc:creator>
	<dc:creator xml:lang="en">Silvianita</dc:creator>
	<dc:creator xml:lang="en">Sade , Juswan</dc:creator>
	<dc:subject xml:lang="en">Censeptual research</dc:subject>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">Payload sabotage</dc:subject>
	<dc:subject xml:lang="en">Offloading FPSO</dc:subject>
	<dc:subject xml:lang="en">Tanker</dc:subject>
	<dc:description xml:lang="en">Artificial Intelligence has experienced significant improvements, including its use in sustainable maritime security. Offloading oil from FPSO to tankers can potentially lose cargo due to sabotage from fraudulent parties. Even though international laws and regulations have been enacted, this potential cannot be controlled. Various approaches and researchers have used Artificial Intelligence in the maritime aspect, but they have yet to discuss it from the perspective of cargo loss. The application of intelligent technology in the offloading process aims to create an integrated and coordinated system of parties involved in oil shipping operations using tankers. Therefore, a systematic review of Artificial Intelligence was conducted to find a solution from the perspective of load loss due to sabotage in the oil offloading process. Overall, we find that the potential for oil sabotage always exists in delivering tankers from the FPSO to their destination. The Automatic Integrated System is the Artificial Intelligence most often used to control maritime security. Instead, we see a need for cloud computing, internet-of-things, and big data analytics, which play a critical role in maritime security today.</dc:description>
	<dc:publisher xml:lang="en">Department of Ocean Engineering, Faculty of Engineering, Hasanuddin University</dc:publisher>
	<dc:date>2026-06-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/maritimepark/article/view/48389</dc:identifier>
	<dc:source xml:lang="en">Maritime Park: Journal of Maritime Technology and Society; Volume 5, Issue 2, 2026;  365-373</dc:source>
	<dc:source>2828-6669</dc:source>
	<dc:source>2828-7010</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/maritimepark/article/view/48389/14629</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Maritime Park: Journal of Maritime Technology and Society</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>