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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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Structural Asymmetry and Collaboration Gap: A Qualitative Case Study Approach to Pentahelix E-Government Implementation in Indonesian Local Government Akbar Muhammad Nur; Alyani Muh. Khair Nurmahdy; Nusri Andi Zulkifli; Nursakti; Muhammad Iqbal
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.427

Abstract

Purpose - This study aims to empirically investigate how Pentahelix collaboration is structured and operationalized in the implementation of E-Government in Soppeng Regency, Indonesia, identify the enabling factors and barriers within the collaborative ecosystem, and evaluate its perceived impacts on local governance and public service delivery. Methods - This study employs a qualitative research design with a single-case study approach. Data were collected from February to April 2025 using three complementary methods: semi-structured in-depth interviews with 25 key informants representing all five Pentahelix stakeholder groups, non-participant observation of coordination meetings and public service points, and document analysis of E-Government roadmaps, policy regulations, and annual reports. Findings -The findings reveal that while the Pentahelix framework contributes to strengthening digital infrastructure and human resource capacity, collaboration remains structurally asymmetric and informal. Academia contributes research and training reactively, the Business sector engages transactionally as vendors, Media serves primarily as a one-way information broadcaster, and the Community participates passively as end-users rather than co-design partners. Persistent challenges include unequal internet access, variable public participation, and a pervasive "collaboration gap" a disconnect between multi-stakeholder inputs and their integration into a coherent transformative strategy. Research Implications – This study demonstrates that the effectiveness of Pentahelix collaboration in E-Government depends on the quality of interaction and integration mechanisms among stakeholders, not merely their formal inclusion, thereby shifting the research focus toward examining collaboration depth, coordination structures, and feedback systems. Originality – This study introduces the novel concept of the “collaboration gap,” providing empirical evidence of the disconnect between multi-stakeholder presence and functional integration while bridging collaborative governance and technology adoption theories in a single analytical framework.
IT Service Quality as A Moderator Between Digital Attendance Application Usage and Employee Performance: Evidence from Indonesian Local Government Nur Aziiza Syam; Asriani; Andi Zulkifli Nusri; Andi Adawiah; Muhammad Iqbal
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.428

Abstract

Purpose - This study aims to analyze the direct influence of digital attendance application (APATOKI) usage and IT service quality on employee performance, as well as to investigate the moderating role of IT service quality in the relationship between application usage and performance within a local government setting. Methods - A quantitative approach with a multivariate regression design was employed. Data were collected from 400 State Civil Apparatus (ASN) personnel in Soppeng Regency, South Sulawesi, who actively used the APATOKI application, selected through purposive sampling based on specific criteria. The instrument utilized a 5-point Likert scale to measure three variables: APATOKI usage, IT service quality, and employee performance. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. Findings -The results demonstrate that both APATOKI usage (β = 0.254, p < 0.05) and IT service quality (β = 0.249, p < 0.05) have a significant positive direct effect on employee performance. More importantly, IT service quality was found to significantly moderate the relationship between APATOKI usage and performance (β = 0.187, p < 0.05). The model explained 69.9% of the variance in employee performance (R² = 0.699). Implications – This study shows that digital performance gains in public sector systems are contingent on the interaction between technology usage and IT service quality, shifting the focus from isolated technology adoption toward integrated system–service ecosystems as drivers of employee performance. Originality – This study provides a novel contribution by empirically demonstrating the moderating role of IT service quality in strengthening the impact of digital application usage on performance, integrating TAM, IS Success Model, and SERVQUAL into a single tested framework within a local government context.
Balancing Benefits and Risks of ChatGPT: Role of AI Ethics, Usage Habits, and Memory Loss in Learning Motivation and Performance Ridwan Daud Mahande; Rosidah
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.v3i2.445

Abstract

This study investigates how AI ethics, ChatGPT usage habits, and memory loss influence learning motivation and learning performance in higher education. The research addresses growing concerns about cognitive and ethical implications of AI tool usage among students, especially in relation to motivation decline and learning outcomes. Although previous studies highlight the benefits of AI tools in enhancing learning, few have explored the negative cognitive and ethical consequences of overuse. This paper fills the gap by examining how learning motivation mediates the relationship between AI-related factors and learning performance through the lens of Self-Determination Theory (SDT). A quantitative, cross-sectional design was employed using purposive sampling. A total of 539 university students who have experience using ChatGPT in academic contexts participated by completing an online questionnaire. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study contributes to the literature by integrating AI ethics, usage patterns, and cognitive outcomes into a unified model of academic motivation and performance. It extends Self-Determination Theory into AI-assisted learning environments and emphasizes learning motivation as a psychological bridge linking ethical and cognitive concerns to learning success. AI ethics, ChatGPT usage habits, and memory loss each have a significant im-pact on both learning motivation and learning performance. Learning motiva-tion serves as a mediating variable in the relationship between all three predic-tors and academic outcomes. Among the predictors, ChatGPT usage habits emerged as the strongest positive influence, whereas memory loss exhibited the most pronounced negative effect. Ethical concerns demonstrated a modest yet statistically significant positive effect, particularly when internalized as responsi-ble academic conduct. Future research should explore longitudinal effects of AI tool use on motivation and cognition, test other theoretical frameworks such as TAM or Cognitive Load Theory, and examine new variables like AI literacy, digital well-being, and academic resilience.
The Concern Over Brain Rot from Generative AI Use Among Preservice Teachers: A UTAUT Approach Ummul Khaeri Masna; Udin Sidik Sidin; Mushaf; Stephen Amukune
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.347

Abstract

Purpose – The increasing use of generative AI on campus has raised concerns about a potential decline in students’ critical thinking skills. While the UTAUT theory is widely used to examine technology adoption, its relationship with the phenomenon of brain rot remains underexplored, particularly among preservice teachers. This study aims to analyze the factors associated with preservice teachers’ intention to use generative AI within the UTAUT framework, as well as to examine its association with tendencies toward brain rot.Method – A quantitative cross-sectional design was conducted with 243 preservice teachers from Universitas Negeri Makassar. Data were collected via a validated 30 item questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between technology adoption constructs and brain rot tendencies.Findings – Social influence was the only significant predictor of behavioral intention to use AI (β = 0.269, p = 0.002). Behavioral intention, in turn, showed a strong positive association with brain rot tendencies (β = 0.817, p < 0.001), explaining 66.7% of the variance (R² = 0.667). Other UTAUT constructs, including performance expectancy and effort expectancy, were not significant predictors. However, given the cross-sectional design, these findings reflect statistical associations rather than causal relationships.Research Implication : Socially driven AI adoption is strongly linked to cognitive passivity, highlighting the need to extend UTAUT with cognitive risk factors and rethink how technology use impacts higher-order thinking.Conclusion – This study indicates that the adoption of AI among preservice teachers is associated with perceptions of declining cognitive abilities. These findings highlight the importance of promoting critical AI literacy and developing assessment approaches that emphasize deep cognitive engagement. Future research is recommended to employ longitudinal designs or incorporate control variables such as digital self-efficacy.
Influence of Innovative Pedagogical Approaches (IPA) on Academic Achievement of Pre-Service Mathematics Teachers in Colleges of Education in Oyo State Sabitu Abiodun; Omenihu George ODUALI
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.370

Abstract

Purpose - This study examines why innovative pedagogical approaches are necessary in mathematics education, particularly to address persistent gaps in academic achievement among pre-service mathematics teachers. It aims to determine the extent to which such approaches improve learning outcomes and whether gender plays a significant role in shaping achievement.Methods – A pre-test and post-test quasi-experimental design was adopted. The population comprised 173 pre-service mathematics teachers in Colleges of Education in Oyo State, with intact classes of 200-level students selected through total enumeration. Data were collected using a self-structured Mathematics Achievement Test, validated by experts, with a reliability coefficient of 0.88 (KR-25). Data analysis was conducted using Analysis of Variance (ANOVA) and multiple regression at a 0.05 significance level.Findings – Results show a significant effect of innovative pedagogical approaches on academic achievement (F(1,172)=122.216; p<0.05). Gender did not show a statistically significant influence despite a high F-value (F(1,172)=103.402; p>0.05). Jointly, innovative pedagogy and gender significantly predicted achievement (F(2,171)=81.084; Adj R²=0.494; p<0.05), explaining 49.4% of the variance.Research implications – The study is limited by its quasi-experimental design and focus on a specific regional context, which may restrict generalizability. The use of intact classes may also introduce uncontrolled variables affecting internal validity.Originality – This study provides empirical evidence quantifying the impact of innovative pedagogy on pre-service teacher achievement. It highlights the limited role of gender and emphasizes the need for structured pedagogical reform, suggesting future research should explore longitudinal effects and broader contexts.
Explaining Continuance Intention in Artificial Intelligence-Supported Learning through Artificial Intelligence Literacy and Technology Acceptance Model Muhammad Nur Faiz; Muhammad Yahya; Abdul Muis Mappalotteng; Sanatang; Fathahillah; Fitria Arifyanti; Selen Subasi
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.372

Abstract

Purpose – Despite the growing use of artificial intelligence in higher education, prior studies have focused more on initial adoption than on continuance intention, while the role of AI literacy in sustaining AI use remains underexplored. This study examines an integrative model linking AI literacy, perceived usefulness, attitude toward using AI, behavioral intention, and continuance intention in AI-supported learning.Methods – A quantitative cross-sectional survey was conducted with 324 students from the Educational Informatics and Computer Engineering and Computer Engineering programs at Universitas Negeri Makassar. Data were collected from January to February 2025 using a 25-item Likert-scale questionnaire and analyzed with PLS-SEM in SmartPLS 4 using bootstrapping with 10,000 subsamples.Findings – All hypothesized relationships were significant: AI literacy → perceived usefulness (β = 0.449, p < 0.001), AI literacy → attitude toward using AI (β = 0.177, p < 0.001), perceived usefulness → attitude toward using AI (β = 0.686, p < 0.001), attitude toward using AI → behavioral intention (β = 0.757, p < 0.001), and behavioral intention → continuance intention (β = 0.800, p < 0.001). Research implications – The cross-sectional design, self-reported data, purposive sampling, and single-institution context limit causal inference and broader generalizability.Originality – This study extends TAM by positioning AI literacy as a competence-based antecedent within a post-adoption sequence that explains continuance intention through perceived usefulness, attitude, and behavioral intention. Practically, the findings suggest that sustainable AI integration in higher education requires not only access to AI tools but also structured support for students’ AI literacy.
Self-Efficacy and School Support on Students' Behavioral Intention to Adopt Learning Technologies: The Mediating Roles of TPACK and Perceived Usefulness Geby Putri Maharani; Alifiya NFH; Ridwan Daud Mahande; Soeharto; Nguyen Van Son
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.403

Abstract

Purpose – Despite the increasing integration of digital technologies in higher education, students’ actual adoption remains inconsistent. Prior studies have predominantly relied on the Technology Acceptance Model (TAM), with limited attention to pedagogical technological competence and the interaction between psychological and institutional factors. This study aims to examine the influence of Self-Efficacy and School Support on students’ Behavioral Intention to use educational technology, with the mediating roles of Perceived Usefulness and Technological Pedagogical Content Knowledge (TPACK).Methods – A quantitative approach with a cross-sectional survey design was employed. Data were collected from 330 university students in South Sulawesi, Indonesia, using an online questionnaire and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM).Findings - The results show that Self-Efficacy significantly influences TPACK (β = 0.751, p < 0.001) and Perceived Usefulness (β = 0.798, p < 0.001), while School Support significantly affects TPACK (β = 0.174, p < 0.001). Furthermore, TPACK (β = 0.603, p < 0.001) and Perceived Usefulness (β = 0.267, p < 0.001) significantly predict Behavioral Intention. Mediation analysis indicates that Perceived Usefulness (β = 0.213, p < 0.001) and TPACK (β = 0.105–0.453, p < 0.001) serve as significant mediators.Research implications – These findings emphasize the importance of enhancing students’ technological competence and strengthening institutional support systems to promote effective technology adoption. However, the study is limited to a specific regional context, which may affect generalizability.Originality – This study extends TAM–TPACK integration by introducing a dual mediation framework that simultaneously examines psychological (Self-Efficacy) and institutional (School Support) factors in university students, highlighting the mediating roles of Perceived Usefulness and TPACK in technology adoption..
IoT-Based Dual-Sensor Vehicle Security System Using Piezoelectric and Glass-Break Detectors with GPS Tracking: Design and Performance Evaluation Zaidan Wafi Rohdyawan; M. Iman Nur Hakim; Raka Pratindy; Mokhammad Rifqi Tsani
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.421

Abstract

Purpose – This study aims to develop an IoT-based vehicle security system using a dual-sensor architecture that integrates piezoelectric and glass-break sensors, complemented by GPS tracking, to detect glass-break–based theft attempts rapidly, accurately, and in real time.Methods – A Research and Development (R&D) approach was employed at the functional prototype stage, involving hardware design, ESP32 programming, vibration response testing, glass-break tests on tempered and tinted glass, GPS accuracy assessment across three environmental conditions, and validation of IoT notification response time via Telegram.Findings - An IoT-based vehicle security system integrating piezoelectric and glass-break sensors demonstrated clear signal separation between normal conditions (ADC < 500) and glass-break events (ADC > 1000), with no overlapping distributions observed during testing. The system achieved real-time detection with an average IoT notification response time of approximately 1.14 seconds and showed near-zero false alarm occurrence under controlled experimental conditions.Research implications – Although the prototype exhibits high sensitivity and specificity in controlled environments, system performance remains influenced by IoT network quality and GPS signal degradation in enclosed spaces. Testing was limited to one vehicle model and two glass types; therefore, further research is required, including large-scale field validation, evaluation in dynamic environments, and the implementation of advanced IoT security protocols.Originality – The main contribution of this study lies in addressing the research gap between prior works that predominantly utilized single-sensor or limited sensor combinations without robust acoustic–mechanical differentiation. The applied integration of piezoelectric and glass-break sensors within an IoT-based architecture establishes a cross-verification mechanism that significantly reduces false alarm potential and enhances detection reliability compared to previous approaches.
Multi-Model LLM Coder with RAG for Indonesian Government Web Content Injection Detection: An Ablation Study in an On-Premise CSIRT Architecture Faishal Azhiman Suryadi; Kalamullah Ramli
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.449

Abstract

Purpose - Government websites in Indonesia face persistent content injection threats, including online gambling embedding, webshell installation, and SEO cloaking, that conventional File Integrity Monitoring (FIM) cannot adequately detect. Existing approaches have not yet integrated multi-model LLM Coder analytics with Retrieval-Augmented Generation (RAG) in an on-premise, host-based architecture tailored for government CSIRT operations. Methods - This study designs, implements, and evaluates a four-zone system integrating an event-driven file monitoring agent (Agent-Watcher), automated orchestration, and a Multi-Model LLM Coder analytics engine augmented with a 16,508-document Qdrant-based RAG knowledge base, fully deployed on-premise. An ablation study evaluated using five metrics (Accuracy, Precision, Recall, F1-Score, and Specificity) compared five models (Qwen 2.5 Coder 7B, CodeGemma 7B, DeepSeek Coder 6.7B, CodeLlama 7B, and StarCoder2 7B) under two scenarios (LLM Only vs. LLM + RAG) using 3,000 unseen PHP, JavaScript, and Python samples. Findings - RAG improved performance in three of five models. CodeGemma 7B achieved the best balanced profile (F1-Score 99.27%), while Qwen 2.5 Coder 7B maintained 100% Precision with zero false positives across languages. DeepSeek Coder 6.7B and StarCoder2 7B degraded under RAG, indicating architecture-dependent RAG compatibility. Research Implication - This study contributes a reproducible all-metric evaluation and proposes a layered deployment strategy (CodeGemma as primary detector, Qwen as validator) for data-sovereign government CSIRT operations.Originality – Existing approaches have not yet integrated multi-model LLM Coder analytics with Retrieval-Augmented Genereation (RAG) in an on premise, host-based architecture tailored for government CSIRT operations.
Speedup, Efficiency, and Scalability of the Ray Framework for Audio Feature Extraction in a Single-Node Virtualized Environment: An Empirical Benchmarking Study Phie Chyan; Sean Coonery Sumarta
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.418

Abstract

Purpose – This study aims to evaluate the performance, speedup, efficiency, and scalability of the Ray framework in a single-node virtualized environment for CPU-bound audio feature extraction tasks.Methods – An empirical benchmarking approach was employed using a dataset of 1,000 audio files with durations of 3–5 seconds. Multiple feature extraction techniques, including MFCC, spectral centroid, spectral rolloff, chroma features, and zero-crossing rate, were implemented using the Librosa library. Performance was evaluated by comparing serial and parallel execution times across different worker configurations.Findings – The results show that execution time decreased from 59.62 seconds in serial execution to 9.86 seconds when using 16 parallel workers, achieving a maximum speedup of 5.98. The system exhibits sub-linear scalability, with efficiency decreasing as the number of workers increases due to task scheduling overhead, resource contention, and virtualization constraints. An optimal performance range is observed at 8–12 workers, where significant speedup is achieved with relatively better efficiency.Research implications – This study demonstrates that the Ray framework challenges the assumption of linear scalability in CPU-bound parallel workloads by revealing how system-level constraints in virtualized single-node environments fundamentally shape speedup and efficiency trade-offs.Conclusion – This study demonstrates that the Ray framework is an effective and practical solution for accelerating embarrassingly parallel, CPU-bound workloads in single-node virtualized environments. While performance improves with increasing parallelism, careful selection of the number of workers is necessary to balance speedup and efficiency. However, the findings are limited by the use of a single-node setup and a relatively small dataset, suggesting that further evaluation in larger-scale or distributed environments is needed.

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