Analisis Komparatif Performa AES-GCM dan ChaCha20-Poly1305 dalam Enkripsi Dokumen PDF Berbasis AEAD

Authors

  • Muhammad Patria Universitas Dian Nusantara, Indonesia
  • Dea Andini Andriati Universitas Dian Nusantara, Indonesia

DOI:

https://doi.org/10.29240/arcitech.v5i1.13645

Keywords:

AEAD, AES-GCM, ChaCha20-Poly1305, PDF Encryption, Algorithm Performance

Abstract

Digital transformation in electronic document services demands encryption mechanisms that are not only cryptographically secure but also performance-efficient. While AES-GCM and ChaCha20-Poly1305 are widely adopted AEAD algorithms, prior research has largely focused on their use in communication protocols or IoT devices, rather than in encrypting PDF documents. This study addresses that gap by empirically comparing both algorithms in real-world digital document processing scenarios. A quantitative experimental method was applied across two scenarios: mass processing of 5,000 small-to-medium PDF files (100KB–8MB), and individual processing of large files (1MB–200MB). Five performance metrics were analyzed: encryption time, decryption time, total processing time, stability, and throughput. Results show that AES-GCM consistently outperformed ChaCha20-Poly1305 across all metrics, offering faster processing and greater stability. Both algorithms produced a constant file size overhead of 28 bytes, which was negligible in terms of storage efficiency. This study contributes to the literature by providing empirical evidence to guide the selection of encryption algorithms in high-performance digital document storage systems.

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Author Biographies

Muhammad Patria, Universitas Dian Nusantara

Fakultas Teknik dan Informatika, Program Studi Teknik Informatika

Dea Andini Andriati, Universitas Dian Nusantara

Fakultas Teknik dan Informatika, Program Studi Teknik Informatika

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Published

30-06-2025

How to Cite

Patria, M., & Andriati, D. A. (2025). Analisis Komparatif Performa AES-GCM dan ChaCha20-Poly1305 dalam Enkripsi Dokumen PDF Berbasis AEAD. Arcitech: Journal of Computer Science and Artificial Intelligence, 5(1), 49–69. https://doi.org/10.29240/arcitech.v5i1.13645

Issue

Vol. 5 No. 1 (2025): June 2025

Section

Articles

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