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Comparison of Digital Forensic Tools for Drug Trafficking Cases on Instagram Messenger using NIST Method

Scientific Journal of Informatics · Vol. 11 No. 4 (2024) · pp. 891–902

DOI
10.15294/sji.v11i4.13463
ISSN
2407-7658, 2460-0040
Published
2024-11-29
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

Purpose: Cybercrime is an unlawful act that utilizes computer technology and the development of the internet. Cybercrime can occur on all electronic devices, including Android smartphones. Forensic handling, particularly mobile forensics, has become crucial in addressing drug trafficking cases conducted through Instagram. As the primary device for accessing Instagram, smartphones store digital data that can serve as evidence in investigations. This research aims to produce a more accurate comparison of results in analyzing Instagram Messenger data containing content related to drug trafficking. Methods: The digital evidence data used in this research included five types of data: text chat, account, image, audio, and image view once. The forensic tools for obtaining digital evidence were MOBILedit, Belkasoft, Mobile Forensic SPF, and Magnet Axiom. The method proposed in this research followed the NIST framework, which consists of four stages: collection, examination, analysis, and reporting. This research followed the NIST framework because it is widely recognized in the field of digital forensics and provides a comprehensive guideline for handling digital evidence. Result: Research results showed that Magnet Axiom had the best performance in digital forensic analysis, with a success rate of 74.1%. MOBILedit Forensic had a success rate of 62.5%, indicating lower performance. Mobile Forensic SPF had a success rate of 44.6%. In comparison, Belkasoft had the lowest success rate of 23.2%, showing that this software could be more effective in detecting and analyzing digital data than the others. Novelty: In this study, the analysis process was conducted using four digital forensic tools, each showing variations in terms of efficiency and effectiveness. Each tool has advantages and disadvantages regarding speed, accuracy, and ability to extract and manage data.

Keywords

Collaboration network of the authors

40 authors · 88 co-authorship links · 8 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Ahmad Dahlan
  • Islamic University of Indonesia
  • Siliwangi University
  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anton YudhanaUniversitas Ahmad Dahlan4
Rusydi UmarUniversitas Ahmad Dahlan4
Abdul FadlilUniversitas Ahmad Dahlan4
Yudi PrayudiIslamic University of Indonesia3
Herman YuliansyahUniversitas Ahmad Dahlan3
Rio AnggaraUniversitas Ahmad Dahlan2
Sunardi SunardiUniversitas Ahmad Dahlan2
Nur WidiyasonoSiliwangi University2
Ahmad LuthfieAffiliation unknown2
Ainunna’imahUniversitas Ahmad Dahlan1
HermanUniversitas Ahmad Dahlan1
FitriahUniversitas Ahmad Dahlan1
Asno Azzawagama FirdausUniversitas Ahmad Dahlan1
Muhajir YunusUniversitas Ahmad Dahlan1
Maulana Muhammad Jogo SamodroUniversitas Ahmad Dahlan1
Bella Okta Sari MirandaUniversitas Ahmad Dahlan1
Dewi SoyusiawatyUniversitas Ahmad Dahlan1
Farhan Radhiansyah RazakUniversitas Ahmad Dahlan1
Imam Mahfudl NasrullohUniversitas Ahmad Dahlan1
Fauzan NatsirAffiliation unknown1
Eri HaryantoIslamic University of Indonesia1
Sahiruddin SahiruddinHasanuddin University1
W YunanriUniversitas Ahmad Dahlan1
Siti HelmiyahUniversitas Ahmad Dahlan1
Abdullah HanifUniversitas Ahmad Dahlan1
Nurmi HidayasariIslamic University of Indonesia1
Aldibangun Pidekso PutroAffiliation unknown1
HERMAN HERMANUniversitas Ahmad Dahlan1
Ruuhwan RuuhwanAffiliation unknown1
Arizona FirdonsyahUniversitas Ahmad Dahlan1
Fahmi AnwarSiliwangi University1
Rusydi UmarAffiliation unknown1
Muhammad Fauzan GustafiAffiliation unknown1
Sunardi SunardiAffiliation unknown1
Yuliani IndrianingsihAffiliation unknown1
Astika AyuningtyasAffiliation unknown1
Haruno SajatiAffiliation 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.unnes.ac.id/journals/sji/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Comparison of Digital Forensic Tools for Drug Trafficking Cases on Instagram Messenger using NIST Method</dc:title>
	<dc:creator xml:lang="en">Nahdli, Muhammad Fahmi Mubarok </dc:creator>
	<dc:creator xml:lang="en">Riadi, Imam </dc:creator>
	<dc:creator xml:lang="en">Biddinika, Muhammad Kunta</dc:creator>
	<dc:subject xml:lang="en">Digital forensic</dc:subject>
	<dc:subject xml:lang="en">Drug trafficking</dc:subject>
	<dc:subject xml:lang="en">Instagram</dc:subject>
	<dc:subject xml:lang="en">National institute of standards and technology</dc:subject>
	<dc:subject xml:lang="en">Smartphone</dc:subject>
	<dc:description xml:lang="en">Purpose: Cybercrime is an unlawful act that utilizes computer technology and the development of the internet. Cybercrime can occur on all electronic devices, including Android smartphones. Forensic handling, particularly mobile forensics, has become crucial in addressing drug trafficking cases conducted through Instagram. As the primary device for accessing Instagram, smartphones store digital data that can serve as evidence in investigations. This research aims to produce a more accurate comparison of results in analyzing Instagram Messenger data containing content related to drug trafficking.
Methods: The digital evidence data used in this research included five types of data: text chat, account, image, audio, and image view once. The forensic tools for obtaining digital evidence were MOBILedit, Belkasoft, Mobile Forensic SPF, and Magnet Axiom. The method proposed in this research followed the NIST framework, which consists of four stages: collection, examination, analysis, and reporting. This research followed the NIST framework because it is widely recognized in the field of digital forensics and provides a comprehensive guideline for handling digital evidence.
Result: Research results showed that Magnet Axiom had the best performance in digital forensic analysis, with a success rate of 74.1%. MOBILedit Forensic had a success rate of 62.5%, indicating lower performance. Mobile Forensic SPF had a success rate of 44.6%. In comparison, Belkasoft had the lowest success rate of 23.2%, showing that this software could be more effective in detecting and analyzing digital data than the others.
Novelty: In this study, the analysis process was conducted using four digital forensic tools, each showing variations in terms of efficiency and effectiveness. Each tool has advantages and disadvantages regarding speed, accuracy, and ability to extract and manage data.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-11-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/13463</dc:identifier>
	<dc:identifier>10.15294/sji.v11i4.13463</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 11 No. 4: Special Issue; 891-902</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v11i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/13463/2052</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>