Habibi
Universitas Prima Indonesia

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EVALUASI STABILITAS ALGORITMA SHA-256, BLAKE2, WHIRLPOOL, SKEIN TERHADAP VARIASI KARAKTERISTIK DATA Yahya Siregar; Rohid Syavelen Pahti; Habibi; Christnatalis HS
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7796

Abstract

Cryptographic hash algorithms play a crucial role in maintaining data integrity and security, particularly in systems processing large amounts of diverse data. However, algorithm selection is often based solely on standards and adoption rates without considering performance stability against variations in data characteristics. This study aims to assess the stability of the SHA-256, BLAKE2, Whirlpool, and Skein algorithms against variations in multimedia data, specifically text, images, music, and video, through two main dimensions: Resource Stability and Statistical Stability. A benchmarking-based computational approach was employed by measuring CPU, memory, latency, and throughput usage over ten runs. The stability level is described using the Coefficient of Variation (CV) to measure the relative variation between runs. The test results show that Whirlpool has the most consistent Resource Stability level with the lowest average CV values ​​for CPU (1.86%), memory (6.67%), latency (2.66%), and throughput (3.04%), which indicates the most stable performance compared to other algorithms. In terms of statistical stability, all algorithms exhibit an avalanche effect approaching ideal conditions (≈50%), an even distribution of hexadecimal outputs, and a bit ratio approaching 50:50 without significant bias. No pure cryptographic collisions were found; hash value similarities were detected by duplicate dataset content. The results confirm that the selection of a hash algorithm should consider the stability of resource utilization in addition to security aspects, especially in systems with high computational loads and diverse data characteristics.