Encrypted network communications reduce the effectiveness of payload based traffic identification and complicate the translation of traffic analysis into network access control decisions. This study evaluates a payload independent workflow that connects flow based multiclass classification with administrator triggered, time limited firewall enforcement. The experiment used the public ISCXVPN2016 benchmark. After removing 18,719 duplicate records, 40,987 unique flows remained; all 23 available numerical flow features were retained without feature selection or normalization. A Random Forest classifier with 300 trees was selected using five-fold cross-validated grid search on a stratified 80% training partition and evaluated on an independent 8,198 sample test set covering 14 VPN and non-VPN traffic classes. The model achieved 88.01% accuracy, 88.00% weighted precision, 88.01% weighted recall, and 87.97% weighted F1-score. It exceeded the strongest reproduced baseline, K-Nearest Neighbors, by 16.09 percentage points in accuracy and 16.29 percentage points in weighted F1-score. Supplemental five-fold evaluation produced a mean accuracy of 88.05% with a 0.36 percentage point standard deviation. The trained classifier was integrated with a web application in which administrators review predicted flows and initiate temporary MikroTik RouterOS rules. All 30 temporary blocking entries observed in the evaluation database reached the Unblocked state with recorded timestamps, demonstrating rule lifecycle traceability at the database level. The findings show that Random Forest can provide competitive flow based classification while supporting an auditable, human controlled access control workflow; however, device level reliability and cross dataset generalizability require further validation.
Wahyu Isnan, Yulia Fatmi, Resmi Darni et al.· Intechno Journal (Informatio...· 0 citations
Vocational learning often faces challenges in fostering active student participation, particularly in practicum-based courses where more than 60% of students tend to be passively engaged during instructional activities, resulting in suboptimal learning outcomes. Therefore, an instructional approach that promotes active involvement is critically needed. This study aims to examine the role of critical thinking and student engagement in predicting learning outcomes within gamified vocational education, where gamification is positioned as a pedagogical approach rather than a statistical variable. This study employed a quantitative approach with an associative research design. The participants consisted of 22 students enrolled in a network programming practicum course, selected using a total sampling technique. Data were collected through Likert-scale questionnaires that met validity and reliability criteria (Cronbach’s Alpha ≥ 0.60). Data analysis was conducted using multiple linear regression with the assistance of SPSS software. The results indicate that critical thinking and engagement simultaneously have a significant effect on learning outcomes (p < 0.05), with a coefficient of determination (R² = 0.909), indicating a strong predictive model. Partially, engagement has a positive and significant effect on learning outcomes (p < 0.05), while critical thinking does not show a significant direct effect (p > 0.05), although it demonstrates a positive relationship. These findings suggest that the effectiveness of gamified vocational learning is more strongly influenced by student engagement as an active learning mechanism, rather than critical thinking as an independent factor.
Baiaturridwan, Dedy Irfan, Resmi Darni et al.· JTP - Jurnal Teknologi Pendi...· 0 citations
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