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All Journal Arcitech: Journal of Computer Science and Artificial Intelligence Bulletin of Network Engineer and Informatics (BUFNETS)
Putri Nabila
Politeknik Negeri Sriwijaya
Computer Science & IT

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Analisis Komparatif Algoritma Naïve Bayes dan XGBoost untuk Mengklasifikasikan Performa Akademik Mahasiswa Berdasarkan Tingkat Ketergantungan AI Aryanti Aryanti; Juriawan Raja Saputra; Taufik Permana; Putri Nabila; Ibnu Asrafi
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17321

Abstract

While Artificial Intelligence (AI) integrates into higher education to streamline information retrieval, comprehension, and academic task completion, over-reliance on these tools may jeopardize student outcomes. Extant literature predominantly examines AI adoption rates, dependency levels, and user sentiments; however, comparative analyses of machine learning models for classifying academic performance relative to AI usage intensity remain scarce. To address this gap, this study evaluates and compares the efficacy of Naïve Bayes and XGBoost algorithms in predicting student performance based on their AI engagement. Utilizing the Academic Outcomes & AI Dependency Analysis Dataset—comprising 8,000 instances and 26 features—the methodology encompasses data preprocessing, normalization, partitioning, model training, and evaluation via accuracy, precision, recall, and F1-score. The empirical results demonstrate that XGBoost outperforms Naïve Bayes, achieving a superior accuracy of 84.17% compared to 79.03%. Consequently, XGBoost proves to be a more robust model for classifying academic performance driven by AI usage, offering valuable insights for the advancement of educational data analytics.
Implementation of The Blake3 Algorithm for Detecting Changes and Ensuring The Integrity of Web-Based Digital Documents Putri Nabila; Aryanti Aryanti; Martinus Mujur Rose
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/2hne3s53

Abstract

The increasing dependence on web-centric digital document management for archiving contracts, academic credentials, and administrative records has intensified anxieties regarding data manipulation and unauthorized alterations. Traditional cryptographic verification methods, including MD5 and SHA-1, are no longer viable due to demonstrated collision vulnerabilities. Meanwhile, more robust alternatives like SHA-256 and SHA-3 frequently encounter performance bottlenecks when processing large volumes of data in real-time web architectures. To address these limitations, this research integrates BLAKE3 a cutting-edge cryptographic hash function that pairs SHA-256-level security with superior throughput via a parallelized Merkle-tree design into a web-based platform dedicated to document integrity verification. Adopting a Research and Development (R&D) methodology framework alongside the Waterfall System Development Life Cycle (SDLC), the project progressed through requirements analysis, system architecture design, deployment, and evaluation phases. The prototype was built using a PHP/Node.js web stack backed by a relational database that logs the BLAKE3 hash of each uploaded asset, recalculating and cross-checking the signatures upon every retrieval query. System performance was assessed via black-box functionality assessments, avalanche effect trials, and hash execution time benchmarks contrasting BLAKE3 against MD5, SHA-256, and SHA-3. The empirical data demonstrates that the platform achieved a 100% success rate in identifying simulated file tampering. Furthermore, BLAKE3 outpaced SHA-256 by a factor of 3.7 to 8.1 depending on file dimensions, while yielding an optimal avalanche effect of 49.9%. These outcomes suggest that BLAKE3 serves as a highly scalable and dependable substitute for safeguarding digital assets within web information systems, particularly for platforms handling high-throughput document verification.
Co-Authors Aryanti Aryanti Ibnu Asrafi Juriawan Raja Saputra Martinus Mujur Rose Taufik Permana