Ilham Asy'ari
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Analisis Data Forensik Pada Rekaman CCTV Menggunakan Metode National Institute Of Standard Techology (NIST) Ilham Asy'ari; Yuhandri; Sumijan
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.7779

Abstract

CCTV (Closed-Circuit Television) recordings have become one of the important instruments in monitoring and securing various places such as companies, commercial buildings, public institutions, and households. CCTV recordings are often vital evidence in investigating crimes, accidents, or other incidents. However, in addition to the visual content stored in CCTV recordings, metadata also plays an essential role in forensic analysis and event reconstruction. The NIST method has developed several techniques and guidelines for forensic metadata analysis on CCTV recordings. This research aims to explore and apply the forensic metadata analysis methods recommended by NIST (National Institute of Standards and Technology) in the context of CCTV recordings. By involving forensic data analysis techniques and information security principles, this study will delve into the potential of metadata analysis in supporting criminal investigations, event reconstructions, and meeting the security standards established by NIST. This research is crucial in the context of digital security and modern forensic investigations. The outcome of applying the NIST methods in forensic data analysis of CCTV recordings is the preparation of an official report derived from the stages outlined in the NIST method, so that the report can serve as a reference in court, and the authenticity of the digital evidence can be validated. By applying the NIST method in forensic data analysis of CCTV recordings, the case handling process becomes structured and adheres to procedures, with a valid report ensuring the integrity of the digital evidence.