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Journal of Vocational, Informatics and Computer Education
Published by PT. Academic Bright Collaboration
ISSN : 29884918     EISSN : 29886325     DOI : https://doi.org/10.66053/voice
Core Subject : Science, Education,
Computer Science & IT Education
1. Informatics and Computing Research addressing the design, development, implementation, and evaluation of computing technologies relevant to educational, professional, and digital learning environments, including but not limited to: Artificial Intelligence and Machine Learning Deep Learning and Neural Networks Data Science, Big Data, and Data Analytics Software Engineering and Software Development Computer Networks and Internet Technologies Cloud Computing and Distributed Computing Systems Internet of Things (IoT) and Smart Systems Human–Computer Interaction (HCI) and User Experience (UX) Intelligent Systems and Decision Support Systems Natural Language Processing and Computational Applications Cybersecurity and Information Security Emerging Computing Technologies and Digital Systems 2. Information Technology in Education Studies focusing on the design, integration, implementation, and evaluation of digital technologies in teaching and learning environments, including: Computer Science Education and Programming Education Artificial Intelligence in Education (AIED) Educational Data Mining and Learning Analytics Intelligent Tutoring Systems and Adaptive Learning Systems Digital Learning Environments and Online Learning Systems Learning Management Systems (LMS) and E-learning Platforms Immersive Learning Technologies (Virtual Reality, Augmented Reality, Extended Reality) Mobile Learning and Ubiquitous Learning Environments Technology-Enhanced Learning (TEL) and Digital Pedagogy Educational Software and Learning System Development Digital Assessment and Technology-Based Evaluation Systems Computational Thinking, AI Literacy, and Digital Literacy in Education 3. Vocational Technology Education Research examining the integration of computing technologies and digital innovation in vocational, technical, and professional education, including: Curriculum Development in Informatics and Computing Education Competency-Based Training and Digital Skill Development Teaching Factory and Industry 4.0 Learning Environments Smart Learning Environments for Technical and Vocational Education Work-Process Knowledge and Workplace Learning Work-Based Learning and Apprenticeship Systems Industry–Education Collaboration in Computing and Technology Fields Workforce Preparation for Digital and Technology-Driven Industries Digital Literacy and Cybersecurity Education in Vocational Contexts Professional Skills Development for the Digital Economy 4. Innovative Digital Learning and Educational Innovation Research exploring innovative pedagogical approaches, emerging technologies, and new learning ecosystems in digital and technology-enhanced education, including: Innovative Digital Pedagogy and Instructional Design Gamification and Game-Based Learning in Computing and Technology Education Project-Based Learning and Problem-Based Learning Supported by Technology Learning Innovation Using Artificial Intelligence and Intelligent Systems Automation and Smart Learning Technologies in Education Digital Transformation in Education and Training Institutions Emerging Educational Technologies and Future Learning Environments Smart Education Ecosystems and Data-Driven Learning Systems Educational Innovation for Developing Digital Competencies and Future Skills
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 88 Documents
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Integrating Digital Skills in Technical and Vocational Education: A Systematic Review in the Context of Industry 4.0 Radita Kurnia; A. Muhammad Syafar; Edarho Oghenevwede Oyovwi
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.475

Abstract

Purpose - This study aims to systematically review how digital skills are integrated into TVET and identify the main strategies and contextual factors influencing their effectiveness. Methods - This study employed a systematic literature review using the PRISMA 2020 framework. Data were collected from the Scopus database, focusing on journal articles published between 2023 and 2025. A total of 34 studies were selected through rigorous screening and eligibility processes. Findings -The review identifies three main strategies for integrating digital skills in TVET: curriculum redesign aligned with Industry 4.0, the adoption of digital learning technologies, and innovative pedagogical models. Successful implementation is influenced by teachers’ digital competence, learner readiness, institutional infrastructure, and policy support. Research Implications - Digital skills integration in TVET is a multidimensional process requiring alignment between curriculum, pedagogy, institutional support, and workforce demands, providing an evidence-based foundation for developing adaptive and sustainable TVET systems in the digital age. Originality - This study provides a systematic and integrated synthesis of recent research (2023–2025) on digital skills integration in TVET by consolidating fragmented findings across curriculum, pedagogy, and institutional dimensions using the PRISMA 2020 framework.
Comparison of the Performance of Support Vector Machines and Naïve Bayes in Analyzing Sentiment Regarding Money Politics in the 2024 General Election Olgraciella Manitik; Irene R.H.T Tangkawarow; Alfiansyah Hasibuan
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.494

Abstract

Purpose – This study aims to compare the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying public sentiment on money politics in the 2024 General Election in Indonesia, as well as to determine which algorithm has the highest accuracy, precision, recall, and F1-score. Methods – A total of 1,280 Indonesian-language tweets from the X platform containing the keyword “money politics” were collected and preprocessed. Of these, 384 tweets were manually labeled by two annotators (Cohen’s Kappa = 0.9493). TF-IDF and SMOTE feature extraction were applied before splitting the data into training (70%) and testing (30%) sets. Model evaluation used accuracy, precision, recall, F1-score, ROC-AUC, paired t-test, and bootstrap confidence intervals. Findings – Naïve Bayes (α=0.1) achieved accuracy 79.31%, precision 70.73%, recall 70.73%, F1-score 0.7073, ROC-AUC 0.8387. SVM (C=1, kernel RBF) achieved 77.59%, 85.71%, 43.90%, 0.5806, 0.8410. Paired t-test (p=0.8326) showed comparable performance across 10-fold CV. Naïve Bayes demonstrated better stability (bootstrap CI [0.5974–0.8101] vs. SVM [0.4193–0.7294]). Research implications – The methodological framework (preprocessing, SMOTE, TF-IDF, tuning, 10-fold CV, paired t-test, bootstrap CI) serves as a reference for election-related text classification in Indonesia. Results offer initial insights for election monitoring agencies (e.g., Bawaslu). Originality – This study provides a comparison of SVM and Naïve Bayes specifically for detecting money politics sentiment in the 2024 Indonesian election, a topic with limited prior research. It applies structured pipeline including manual labeling (high inter-annotator agreement), SMOTE, and statistical validation (paired t-test, bootstrap CI).
Explainable Clinical-Operational Intelligence for Hospital Length of Stay Prediction Using Integrated Multi-Source Admission Data with Time-Based Evaluation Dwi Putro Sarwo Setyohadi; Hendra Yufit Riskiawan; Aji Seto Arifianto; I Gede Wiryawan; Akas Bagus Setiawan
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.507

Abstract

Purpose - Hospital length of stay (LOS) affects bed turnover, discharge planning, staffing, and capacity. Integrated hospital data can strengthen LOS prediction and support decision-making. This study developed an explainable clinical-operational intelligence framework for LOS prediction using integrated admission data. Methods - The dataset comprised 45,000 admissions with supporting patient, diagnostic, prescription, billing, ward, bed, staff, and insurance records. It is based on a structured simulation designed to resemble the operational data of hospitals. An admission-level master table was constructed from demographic, temporal, clinical, pharmaceutical, insurance, operational, and patient history features. Length of stay (LOS) regression and high-risk LOS classification were evaluated using a temporal split of 2020-2023 for training, 2024 for validation, and 2025 for testing. Ridge, Random Forest, XGBoost, and CatBoost were compared, followed by threshold optimization, label screening, and SHAP analysis. Findings – CatBoost achieved the best LOS regression performance, with a test MAE of 1.606, an RMSE of 2.028, and an R2 of 0.614. For classification, very_high_los_q90 produced the most balanced extreme-risk formulation, with an accuracy of 0.885 and ROC-AUC of 0.802, whereas high_los_q75 yielded a recall of 0.998 and an F1-score of 0.604. SHAP indicated that prior admission history, diagnostic burden, medication-related features, and ward-level context were prominent drivers of LOS. Research implications – Integrated hospital data are useful for detecting prolonged and extreme LOS, supporting better hospital planning and resource management Originality – This study offers an explainable modeling approach using integrated admission data to support LOS prediction and hospital analytics
Exploration and Comparison of Machine Learning Algorithms for Classifying Supply Categories in a Motorcycle Oil Parts Dataset Redi Ahmad Putra Nuranto; Dida Diah Damayanti; Sri Martini
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.651

Abstract

Purpose – This study compares five machine learning algorithms for predicting supply and sales categories in the Honda oil spare parts supply chain in Banten. Using an exploratory data mining approach, the models were evaluated through confusion matrix analysis and optimized using hyperparameter tuning to improve classification accuracy and model performance. Methods – This study uses a data mining approach with data selection, preprocessing, transformation, modeling, and evaluation. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score metrics. Model optimization was implemented using one of the hyperparameter tuning techniques, that is random search cross validation. Findings – Random Forest and XGBoost achieved identical and the highest performance after hyperparameter tuning, with an accuracy of 0.8061. Decision Tree showed a very close performance with an accuracy of 0.8060. Neural Network achieved an accuracy of 0.7939, while Naïve Bayes recorded the lowest performance at 0.5515. Overall, hyperparameter optimization improved the performance of most models, demonstrating its effectiveness in enhancing classification accuracy across different algorithms. Implications – This study provides a systematic machine learning framework for supply category classification and demonstrates the practical potential of machine learning and hyperparameter tuning in analyzing Honda motorcycle oil spare part supply patterns using historical operational data. Originality – This study presents a structured approach that compares various algorithms but also integrates confusion matrix-based evaluation and hyperparamter tuning optimization. Nevertheless, the optimised model requires further validation using a cost- and operationally-sensitive evaluation approach to assess the actual business impact under real-world supply chain conditions.
Application of the Simple Additive Weighting (SAW) Method to Determine Work Placement Readiness of Vocational Training Institution Students (Case Study: LPK Global Partner Bridge) Arrasyid Alifyan Cahaya; Arny Lattu; Carti Irawan
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.669

Abstract

Purpose – This study applies the Simple Additive Weighting (SAW) method within a Decision Support System (DSS) to assess work placement readiness of students at LPK Global Partner Bridge, a language and communication skills training institution preparing students for overseas employment, and generate structured recommendations. Methods – A quantitative descriptive approach was used with total sampling of 108 students from the 2025 cohort. Data were derived from attendance and final training scores using five criteria: Attendance, Script and Vocabulary, Conversation and Expression, Listening, and Reading. Weights were assigned through expert judgment and normalized proportionally. A threshold of 0.70, based on Indonesia’s Minimum Competency Standard (KKM), was applied. The SAW process included decision matrix construction, Max normalization, and weighted summation to obtain preference values (Vi), implemented in Microsoft Excel. Findings – Preference values ranged from 0.5061 to 0.9864, with a mean of 0.8036. A total of 92 students (85.19%) were classified as Ready, while 16 students (14.81%) were categorized as Not Ready. Research Implications – The model offers a structured and data-driven evaluation framework but is limited to quantitative criteria and excludes non-technical factors such as motivation and psychological readiness. Originality – Unlike prior SAW studies that primarily focus on ranking alternatives, this study extends the method to categorical classification of work placement readiness (Ready / Not Ready), using institutional training data. This approach enables more practical decision support for placement evaluation, providing a replicable framework for vocational training institutions.
Implementing Interactive Virtual Simulation Media to Reduce Procedural Errors in Learning Motorcycle Electrical Systems in Vocational Education Wabdillah; Nurhijrah
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.670

Abstract

This study examines changes in students’ procedural accuracy after participation in interactive virtual simulation-based learning in motorcycle electrical system instruction within vocational education. The study addresses the persistent issue of low procedural accuracy among vocational students, particularly in diagnostic sequencing, wiring interpretation, tool utilization, and troubleshooting procedures. A quasi-experimental one-group pretest–posttest design was employed involving 90 vocational students from three vocational high schools in South Sulawesi, Indonesia. Data were collected using a procedural error assessment instrument consisting of performance-based troubleshooting tasks and structured observation checklists administered under equivalent assessment conditions during the pretest and posttest. The intervention was conducted over six weeks using interactive virtual simulation media integrating guided troubleshooting procedures, visual circuit representation, and immediate corrective feedback. Data analysis included descriptive statistics, Shapiro–Wilk normality testing, paired sample t-test analysis, and Cohen’s d effect size estimation. The findings indicated substantial improvement in students’ procedural performance after participation in the simulation-based learning activities. The mean procedural accuracy score increased from 61.24 during the pretest to 82.67 during the posttest, while the average procedural error frequency decreased from 7.3 to 2.1 errors per student. The paired sample t-test revealed a statistically significant difference between pretest and posttest scores, t(89) = 18.42, p < 0.001, with a large effect size (d = 1.85). Nevertheless, because the study employed a one-group design without a control group, the findings should be interpreted cautiously and do not establish definitive causal superiority of the intervention over conventional instruction. Overall, the findings suggest that interactive virtual simulation media may support procedural learning and troubleshooting practice in vocational automotive education. The study contributes to the literature by emphasizing procedural error reduction as an important indicator of technical competency development in motorcycle electrical system learning.
Integrative Active Learning and Critical Thinking Development in Indonesian Higher Education: A Quasi-Experimental Study Besse Qur&#039;ani; Israwati Hamsar
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.677

Abstract

Critical thinking is widely recognized as an essential competency in higher education, particularly in disciplines that require analytical reasoning and contextual problem-solving. This study investigated the effect of an integrative active learning approach on the development of critical thinking skills among undergraduate students in the Family and Consumer Sciences Education program at an Indonesian university. A quasi-experimental design with a nonequivalent control group was employed involving 60 students divided into experimental and control groups. The intervention integrated group discussions, problem-based learning (PBL), and structured presentations supported by basic instructional technology, including projectors and presentation software, while the control group received conventional lecture-based instruction. Data were collected using an adapted critical thinking instrument measuring five dimensions: analysis, inference, evaluation, explanation, and interpretation. The results of the paired sample t-test indicated a substantial improvement in the experimental group (p = 0.000), whereas the control group also demonstrated a statistically significant but smaller gain (p = 0.032), suggesting that conventional instruction may still contribute to incremental critical thinking development. An independent sample t-test further revealed a significant difference between the post-test scores of both groups (p = 0.000). The calculated Cohen’s d value of 1.94 indicated a very large intervention effect, although this finding should be interpreted cautiously considering the short intervention duration and limited sample size. Unlike many previous studies that focus on isolated active learning strategies or technology-intensive environments, this study examines the implementation of an integrative active learning framework within a resource-constrained higher education context. The findings suggest that structured active learning supported by modest technological resources can effectively promote critical thinking development in Indonesian higher education settings.
Implementing Local Server Migration to Free Hosting to Increase Web Service Availability Arif Hidayat; Ismail Puji Saputra
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.684

Abstract

Objective - This research aims to implement the migration of on-premises servers to a hosting service to improve the availability and accessibility of a student data management application. Methods - The approach used is the Network Development Life Cycle (NDLC) framework, which includes the stages of analysis, design, simulation, implementation, monitoring, and management. The success of the migration was evaluated by validating the integrity of all directory files, synchronizing records in the MySQL database, and testing the functionality of the application's navigation features in the new server environment. Findings - The test results indicate that the migration was successfully implemented in accordance with the established technical parameters. Monitoring data over ten days showed an average response time ranging from 1.2 to 2.5 seconds, with an average service availability (uptime) of 98.7%. Research Implications - These findings confirm that hosting services can be an efficient infrastructure solution for developers or institutions to distribute small-scale applications globally without significant initial costs. Conclusion - The application of the NDLC methodology provides a systematic approach to ensuring the smooth transition of data from on-premises to the cloud. Despite the high availability level, it is recommended to consider infrastructure scalability in the event of a significant increase in traffic in the future
Hybrid Chi-Square and Binary Particle Swarm Optimization Feature Selection with Prior-Corrected Multinomial Naive Bayes for SMS Spam Detection Chrisandito Sebastian Erlangga Bia; Jumanto Unjung
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.730

Abstract

Purpose – SMS spam remains a persistent cybersecurity threat, with 68% of mobile users exposed to unsolicited messages. Existing lightweight classifiers suffer from two compounding problems: feature representations that fail to capture semantic spam patterns, and class imbalance that biases probabilistic classifiers toward the majority class. This study proposes a unified pipeline that resolves both problems simultaneously. Methods – A dual feature extraction scheme combining TF-IDF with 12 empirically validated semantic features feeds a two-stage Chi-Square and Binary Particle Swarm Optimization (BPSO) feature selection pipeline. A Prior-Corrected Multinomial Naive Bayes (PC-MNB) recalibrates class priors at inference time to counteract Random Oversampling bias. Experiments were conducted on the UCI SMS Spam Collection. Findings – The proposed model achieved 98.07% accuracy, 95.45% macro F1, and 96.64% spam precision with only 4 false positives across 903 legitimate messages reducing false alarms by 89.5% over the strongest baseline. Research implications – Evaluation is limited to English-language SMS; generalization to multilingual corpora remains unvalidated. The rule-based semantic features are brittle against adversarial obfuscation, and BPSO incurs a one-time offline training cost of 10–25 minutes. Originality – This study is the first to integrate dual semantic-statistical feature extraction, filter-wrapper hybrid selection, and inference-time prior correction into a single CPU-deployable pipeline for SMS spam detection, distinguished from prior CS-BPSO work by domain, feature architecture, and probabilistic calibration mechanism. Future work will explore multilingual validation and SHAP-based explainability.
The Effectiveness of Interactive Digital Media in Enhancing Students’ Competence in Hair Bun Styling Instruction Israwati Hamsar
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.734

Abstract

This study aimed to examine the effectiveness of video-based interactive digital media in enhancing students’ competence in hair bun styling instruction within cosmetology education. Grounded in Mayer’s Cognitive Theory of Multimedia Learning and Bandura’s Social Learning Theory, the study addresses the need for more engaging, flexible, and repeatable instructional approaches in practice-based vocational learning environments. A quasi-experimental study employing a non-equivalent control group design was conducted involving 12 undergraduate students enrolled in the Family Welfare Education Study Program. Baseline equivalence between the experimental and control groups was confirmed through comparable pretest results. The experimental group received instruction through researcher-developed interactive multimedia modules containing procedural video demonstrations, replay functions, segmented guidance, and zoomed visualizations, while the control group was taught using conventional demonstration methods. The intervention was conducted across four instructional sessions over four consecutive weeks. Data were collected using a performance-based practical assessment rubric and analyzed using descriptive statistics, N-Gain analysis, paired and independent sample t-tests, and Cohen’s d effect size analysis. The findings revealed that the experimental group achieved higher posttest scores (M = 85.83) compared to the control group (M = 72.50). The N-Gain score of the experimental group (0.62) was categorized as moderate-to-high, whereas the control group (0.28) was categorized as low. Statistical analysis showed a significant difference between groups (p < 0.05) with a large practical effect size (Cohen’s d = 1.84). The findings indicate that interactive digital media effectively improves procedural understanding, psychomotor performance, and aesthetic competence in hair bun styling instruction. Although the small sample size and contextual nature of the intervention limit broader generalization, this study contributes empirical evidence supporting the integration of multimedia learning technologies into vocational cosmetology education and provides a foundation for future research involving more advanced digital learning environments.

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