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Journal : Arcitech: Journal of Computer Science and Artificial Intelligence

Perancangan Aplikasi Tabungan Siswa TK Al-Hurriyah Berbasis Web Patria, Muhammad; Bawafi, Imam; Rahayu, Adellya; Sihombing, Feny Hertati; Syahindra, Wandi
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 3 No. 1 (2023): June 2023
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v3i1.8132

Abstract

The purpose of this study was to design a student savings application at TK al-Hurriyah. The problem that arises is that the savings data management system that is currently running is still being done manually, recording and storing data is written in a book, and the calculations are still being done with a calculator. Based on the findings that show that the lack of an existing system indicates a deficiency in the software section to help manage savings data. So it is necessary to have a separate system for student savings, namely a web-based student savings information system. This study uses the system requirements method and system modeling using UML (Unified Modeling Language) to describe visually, which is then implemented with the PHP programming language (Hypertext Preprocessor) with MySQL database as the database used. The benefits of this research are expected to facilitate the process of collecting data on savings of TK al Hurriyah students and storing savings data, so that the information you want to get later will run quickly and accurately.
Analisis Komparatif Performa AES-GCM dan ChaCha20-Poly1305 dalam Enkripsi Dokumen PDF Berbasis AEAD Patria, Muhammad; Andriati, Dea Andini
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 1 (2025): June 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i1.13645

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.