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All Journal JUITA : Jurnal Informatika bit-Tech
Lilik Tiara Giantri
Yarsi Pratama University
Computer Science & IT

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Vulnerability Assessment of Information Disclosure in Bimasoft CBT Muhammad Hudzaifah Nasrullah; Tilly Raycitra Widya; Lilik Tiara Giantri; Duta Arief Christanto; Dede Cahyadi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2838

Abstract

This research examines the security parameters of Bimasoft CBT, a prominent computer-based testing platform utilized extensively in Indonesia, particularly during the execution of UNBK and amid the Covid-19 pandemic. Although CBT systems present distinct advantages in terms of efficiency relative to traditional paper-based assessments, they concurrently introduce significant security concerns. This issue is particularly pertinent considering research indicating that students exhibiting high self-efficacy tend to be more inclined towards dishonest practices, potentially capitalizing on system vulnerabilities. The investigation concentrates on the “offline self-simulation” iteration of Bimasoft CBT, which permits autonomous hosting capabilities. The assessment methodology incorporated strategic planning, a technical examination of the system, identification of vulnerabilities utilizing tools such as Chrome DevTools and Burp Suite, and risk evaluation employing the CVSS 4.0 framework. The inquiry revealed two medium-risk vulnerabilities (CVSS score: 6.9) that jeopardize confidentiality, permitting students to access examination questions prior to login and secure tokens without the oversight of a supervisor. To address these concerns, three principal solutions are recommended: the implementation of back-end token validation, the restriction of access to examination questions via the WordPress REST API prior to login, and the avoidance of CSS for concealing critical content. These findings underscore the necessity of fortifying security within CBT systems to ensure equitable assessment, uphold academic integrity, and assist developers and policymakers in the advancement of digital examination platforms.
Performance Evaluation of Tuned and Untuned Machine Learning Models in Speech Emotion Recognition Muhammad Hudzaifah Nasrullah; Dede Cahyadi; Tilly Raycitra Widya; Ewin Suciana; Lilik Tiara Giantri
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.29015

Abstract

This analysis takes on a comparative review of three distinct machine learning approaches: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) to ascertain emotional states in verbal communication by utilizing the RAVDESS resource. In this review, we perform a strategy that unites audio feature extraction, model training with or without tweaks to hyperparameters, and evaluation via metrics including accuracy, precision, recall, and F1-score. The assessment shows that, before any refinement, SVM secured the utmost accuracy of 79%, trailed by MLP at 76% and RF at 71%. Following optimization, only SVM exhibited an enhancement, reaching 80%, whereas MLP and RF displayed negligible or no improvement. An examination of the confusion matrix revealed that SVM produced the most uniformly distributed predictions and effectively reduced misclassification errors, particularly within the emotion categories of “calm” and “happy.” This investigation offers empirical substantiation of SVM as a robust baseline model for speech emotion recognition in localized settings, while simultaneously providing insights into model optimization and development that could inform future implementations in speech-based human–computer interaction.
Co-Authors Dede Cahyadi Duta Arief Christanto Ewin Suciana Muhammad Hudzaifah Nasrullah Tilly Raycitra Widya