JuSTISe: Journal Data Science, Technology, Informatics and Security
Vol 1 No 1 (2023): Journal Data Science, Technology, Informatics and Security (Juni 2023)

Analisis Digital Forensik pada Digital Footprint untuk Identifikasi Pelaku Cybercrime dengan Framework FDFI

Bunga Islamiya Putri (Universitas Kebangsaan Republik Indonesia)
Deni Suprihadi (Universitas Kebangsaan Republik Indonesia)



Article Info

Publish Date
26 Jun 2023

Abstract

The large number of social media users at this time, resulting in many crimes (cybercrime), and the difficulty of identifying cybercrime perpetrators on social media because most of them are done anonymously (using fake accounts). However, the digital footprint left by users can facilitate the process of identifying perpetrators. This study uses the Social Network Analysis (SNA) Method to collect data and approach social media then uses the Digital Forensics Investigation Framework (FDFI) in conducting investigative analysis. The study will analyze digital evidence searches on the Twitter app. The main finding of the study is that this approach is able to identify digital traces with high accuracy and associate them with the identity of the perpetrator. The results of this study have an important impact on law enforcement and cyber crime prevention. Effective perpetrator identification through digital footprint analysis can assist law enforcement agencies in taking swift and accurate action. In addition, this research can also be the basis for the development of more sophisticated and adaptive forensic digital analysis methods in the face of technological developments and new crime methods in cyberspace.

Copyrights © 2023






Journal Info

Abbrev

justise

Publisher

Subject

Description

JuSTISe: Journal Data Science, Technology, Informatics and Security adalah jurnal ilmiah nasional yang ditinjau oleh sejawat (peer-reviewed) dan diterbitkan oleh Universitas Kebangsaan Republik Indonesia. Jurnal ini berfokus pada publikasi hasil penelitian berkualitas tinggi di bidang ilmu data ...