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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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Determinants of Employment Social Security Participation among Informal Workers: Evidence from a Cross-Sectional Study in Riau Lira Mufti Azzahri Isnaeni; Putri Ayuni Alayyannur; Prasetyawati
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.v4i1.599

Abstract

Purpose – Expanding BPJS Ketenagakerjaan coverage in Indonesia remains difficult, especially among informal workers not enrolled through mandatory employer schemes.Scholars note that both cognitive and emotional factors probably shape voluntary enrollment in social protection, but evidence linking these dimensions to employment-based insurance remains scarce. Methods – The research is a type of quantitative research with a cross-sectional design used to explore how knowledge, attitudes, and beliefs correlate with the participation of informal workers in the BPJS Employment program. A cross-sectional survey of 390 purposively sampled informal-sector participants was conducted. Trained enumerators collected data with a structured questionnaire, which was then analysed by multiple linear regression. Findings –Regression findings indicate that knowledge (B = 0.320, p < 0.001), attitude (B = 0.410, p < 0.001), and trust (B = 0.500, p < 0.001) each positively predict enrollment, with trust showing the strongest effect. The global model is significant (F = 28.350, p < 0.001), thus the three variables together account for a meaningful share of the variance. Enhancing informal workers' uptake of BPJS Ketenagakerjaan demands more than spreading facts; it also calls for cultivating supportive attitudes and building trust in the scheme's agencies Research implications – This study points to multi-faceted approaches, education, direct outreach, and reputation strengthening, that respect the unique patterns and challenges of the informal labour sector. Originality – Its approach specifically examines the determinants of informal sector workers' participation in employment social security programs in Indonesia through a comprehensive cross-sectional study design. This research makes a novel contribution by integrating sociodemographic, economic, social security literacy, and occupational risk perception variables into a single empirical analytical framework rarely explored simultaneously in the context of informal workers.
Real-Time Intelligent IoT-Based Drum Brake Temperature Monitoring System Maulana Yusuf Alkahfi; Raka Pratindy; M. Iman Nur Hakim; Nanang Okta Widiandaru
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.v4i1.601

Abstract

Purpose – This study addresses brake system failures in heavy vehicles caused by excessive thermal buildup in drum brakes. Existing monitoring systems rely on single-parameter sensing and lack early warning capabilities, thereby increasing the risk of brake fade and accidents. This study aims to develop a real-time monitoring system to improve safety. Methods: A Research and Development (R&D) approach was applied, including system design, implementation, and testing. The proposed system integrates a Raspberry Pi 4 Model B, Type K thermocouple, ESP32-C3 Super Mini, and GPS NEO-6M module. The data were transmitted via the Thingspeak IoT platform and displayed on a 7-inch TFT touchscreen. Experimental validation includes thermocouple calibration, GPS speed testing, and IoT latency measurement Findings – The thermocouple achieved a mean absolute error of 7.2°C and a percentage error of 3.4% (96.6% accuracy). The GPS speed measurement showed a 2.6% error (97.4% accuracy). IoT latency ranged from 1.2–2.0 s, with 100% data transmission success. The system reliably triggered alerts when the temperature exceeded 360°C, confirming effective real-time monitoring. Research implications: Limitations include dependence on Internet connectivity, environmental effects on sensors, and scalability challenges. Future work should focus on improving robustness and integrating predictive features. Originality – The developed system demonstrates reliable performance at the prototype level. However, the validation was conducted under controlled conditions using a single sensor and without vehicle load. Therefore, further validation under varying load conditions, road gradients, and multipoint brake measurements is required before practical large-scale deployment.
Design and Implementation of a Real-Time IoT-Based Hydroponic Monitoring System Using Deep Flow Technique with Web Analytics Ramdhan Setiadhi; Imam Taufik; Candra Adipradana; Afifah Nurul Izzati
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.v4i1.516

Abstract

Purpose – This study addresses the challenge of maintaining optimal nutrient and environmental conditions in indoor hydroponic cultivation, where temperature, humidity, light intensity, and nutrient concentration interact dynamically. Many existing systems still depend on manual observation or fragmented monitoring, which limits real-time responsiveness and data-driven decision-making. Therefore, this study aims to design and implement a real-time Internet of Things (IoT)-based hydroponic monitoring system using the Deep Flow Technique (DFT) with an integrated architecture for continuous monitoring and analytical interpretation. Methods - An end-to-end IoT monitoring system was developed by integrating sensor hardware, wireless communication, backend processing, and a web-based analytics dashboard. The TDS, DHT21, and BH1750 sensors were connected to an ESP8266 microcontroller for real-time data acquisition and Wi-Fi transmission at 60-second intervals. The backend used NestJS with PostgreSQL storage, and a ReactJS dashboard visualized real-time and historical data. Monitoring will be conducted from late May to mid-June 2025. Findings - The system consistently captured environmental and nutrient data in real time. Nutrient concentration ranged from approximately 400 to 1,100 ppm, temperature from 27–29 °C, humidity from 65–80%, and light intensity from 180–4,780 lx. The data showed consistent temporal patterns and confirmed the system’s capability of monitoring dynamic hydroponic conditions. Research Implications - The system remains limited to monitoring without automated control, pH measurement, sensor drift evaluation, or non-Wi-Fi deployment. Originality – This study contributes an integrated end-to-end IoT architecture that combines real-time sensing, structured data management, and web-based analytics for indoor DFT hydroponics.
Optimization of Fertilizer and Pesticide Efficiency Based on Hybrid Artificial Intelligence to Increase Rice Production Hamid Wijaya; Rima Ruktiari; Rizal Fani
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.v4i2.725

Abstract

Purpose – This study aims to develop a Hybrid Artificial Intelligence model integrating Artificial Neural Network (ANN) and Genetic Algorithm (GA) to optimize fertilizer and pesticide efficiency while improving rice production under diverse agricultural conditions. The research addresses the limitations of conventional agricultural input management, which often relies on generalized cultivation practices and leads to inefficient resource utilization and environmental degradation. Methods – The study employed a quantitative predictive and optimization-based experimental design using 1,080 rice cultivation records collected from Southeast Sulawesi Province, Indonesia. The dataset included agronomic and environmental variables such as soil pH, rainfall, pest infestation intensity, fertilizer dosage, pesticide dosage, and rice yield. ANN was utilized to predict rice production patterns, while GA was implemented to optimize fertilizer and pesticide dosage combinations. Model performance was evaluated using MAPE, MSE, RMSE, MAE, and coefficient of determination (R²). Findings – The ANN model demonstrated strong predictive capability with a MAPE value of 3.27%, RMSE of 0.22, and R² value of 0.53, indicating its ability to capture complex non-linear relationships among cultivation variables. Furthermore, the hybrid ANN-GA model successfully optimized agricultural input usage by reducing fertilizer dosage by 76.61% and pesticide dosage by 66.32%, while increasing predicted rice production from 4.28 tons/ha to 6.09 tons/ha. These results indicate that hybrid AI systems can improve agricultural efficiency and support sustainable rice production management. Research implications – This study contributes theoretically to the advancement of hybrid AI applications in precision agriculture by integrating predictive learning and adaptive optimization within a unified framework. Practically, the findings provide an intelligent decision-support model that may assist farmers and agricultural stakeholders in improving productivity, reducing excessive chemical input usage, and promoting environmentally sustainable farming practices. Originality – The originality of this study lies in its contribution to the advancement of hybrid AI applications in precision agriculture through the integration of predictive learning and adaptive optimization within a unified framework.
Ethical Awareness and Perceived Risk in Generative AI Adoption: An Extended UTAUT Study among University Students Dian Puspita Sari Andri; Muhammad Fajar B
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.v4i1.358

Abstract

Purpose – This study aims to extend the UTAUT by integrating ethical awareness and perceived risk to address the limitations of approaches that have predominantly emphasized usefulness and ease of use in explaining generative AI usage behavior. The study shows that ethical considerations not only complement existing factors but may also function as a direct determinant in shaping technology usage behavior. Methods – This study employed a quantitative survey with a cross-sectional design involving 327 students at Universitas Negeri Makassar. Data were collected through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings – The structural model results indicate that ethical awareness has a significant effect on usage behavior (β = 0.377; p < 0.001), suggesting that students with higher ethical awareness tend to use generative AI more responsibly. Social influence significantly affects behavioral intention (β = 0.618; p < 0.001) and use behavior (β = 0.194; p = 0.012). Behavioral intention also influences use behavior (β = 0.168; p = 0.002), while facilitating conditions have a positive effect on usage behavior (β = 0.193; p = 0.012). In contrast, perceived risk does not show a significant effect (β = 0.057; p = 0.256). Research implications – The cross-sectional design and the use of a single institutional sample limit causal inference and the generalizability of the findings. Originality – This study extends UTAUT by integrating ethical awareness and perceived risk. The key novelty lies in identifying ethical awareness as a direct determinant of use behaviour, highlighting the central role of ethical factors in generative AI adoption, while perceived risk shows limited influence.
Self-Efficacy over Trust: Rethinking Sustainable AI Use among University Students Vina Annisa Sofyan; Mustari S. Lamada; Sanatang; Shabrina Syntha Dewi; Muh. Akbar
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.v4i1.402

Abstract

Purpose – Artificial intelligence (AI) is increasingly embedded in higher education, yet sustained use remains insufficiently explained by adoption-centered models that primarily emphasize direct effects.This study examines continuous AI use among university students by integrating perceived enjoyment, effort expectancy, trust in artificial intelligence, and AI self-efficacy within a parallel-mediation framework. Methods – A quantitative cross-sectional design was employed involving 247 undergraduate students who reported varying levels of AI use in academic contexts. Data were collected through an online questionnaire and analyzed using partial least squares structural equation modeling (PLS-SEM) to assess direct and indirect relationships. Findings – Perceived enjoyment (β = 0.386) and effort expectancy (β = 0.363) significantly predicted continuous AI use, with perceived enjoyment showing the stronger direct effect. AI self-efficacy significantly mediated both relationships, particularly the effect of effort expectancy (β = 0.297). By contrast, trust significantly mediated only the perceived enjoyment pathway (β = 0.196), while its mediating role in the effort expectancy pathway was not supported under the conventional significance criterion. Research Implications -The study suggests that higher education institutions should strengthen students’ AI self-efficacy while designing AI-supported learning environments that are both intuitive and engaging. Originality - The study contributes to post-adoption AI research by clarifying the relative mediating roles of self-efficacy and trust, highlighting that AI self-efficacy is a more robust mechanism than trust in sustaining AI use among university students.
Implementing the SAW Method in a Web-Based Decision Support System for Study Program Selection: Development, Testing, and Evaluation at a Private University in Indonesia Ahmad Ryadussholihin Syafi’i; Asmaul Husna RS; Sirajun Nasihin; Mindi Richia Putri; M. Dermawan Mulyodiputro
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.v4i1.414

Abstract

Purpose – This study aims to address the problem of inappropriate study program selection among prospective university students by developing a web-based decision support system (DSS) using the Simple Additive Weighting (SAW) method to generate objective and structured recommendations. Methods – The research employs a system development approach by implementing the SAW method in a web-based DSS. The system evaluates four cognitive criteria, namely verbal ability (weight 0.25), numerical ability (weight 0.30), logical reasoning (weight 0.25), and spatial ability (weight 0.20), applied to four D3 study program alternatives. Each criterion was weighted, normalized, and calculated to produce a ranking of study program alternatives. Data were collected through questionnaires and institutional academic records. Findings – A pilot test involving 40 high school students showed an 85% alignment rate, where most participants considered the recommendations appropriate to their interests. User acceptance scores ranged from 4.25 to 4.32 out of 5 across usability, interface clarity, and recommendation usefulness, indicating a positive initial reception. These results suggest the system demonstrates preliminary feasibility as a structured recommendation aid, though broader generalization requires further testing. Research Implications – The findings suggest that multi-criteria DSS can enhance the objectivity and consistency of study program recommendations and can be practically implemented in higher education admission processes. Originality – This study contextualizes the SAW method within a specific private university setting, applying it to four D3 study program alternatives using four cognitive criteria weights established through institutional input. The contribution lies in combining system implementation, functional testing, and user acceptance evaluation into a documented development framework applicable to higher education admission support contexts.
Perceptions of Grammar-Focused Mobile Applications Among HSC Learners Across Bangladesh: An Exploratory Mixed-Methods Study Tanvir Mostafa
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.v4i2.436

Abstract

Purpose – This study investigates the perceptions of Higher Secondary Certificate (HSC) students in Bangladesh toward grammar-focused mobile applications, addressing the gap in research on secondary-level learners and grammar-specific mobile learning within diverse geographical contexts. Methods – A mixed-method exploratory design was employed involving 50 students from 16 colleges across 16 districts representing all 8 divisions of Bangladesh. Quantitative data were collected באמצעות a structured questionnaire and analyzed descriptive statistics, while qualitative insights were obtained from semi-structured interviews with 10 participants and analyzed thematically. Findings – The results indicate generally positive perceptions toward grammar apps, with 78% of students agreeing that apps enhance engagement and 72% reporting increased motivation. Interactive exercises (86%), user interface (80%), and feedback (76%) were identified as the most helpful features. However, limitations include insufficient explanatory depth, limited contextual examples (only 60% rated as helpful), dependence on internet connectivity, and tendencies toward over-reliance on automated correction. Students also reported improved confidence in grammar application, particularly in writing tasks. Research implications – The study is limited by its small purposive sample, reliance on self-reported perceptions, and absence of formal reliability measures, which restrict generalizability. Nonetheless, it highlights the need for integrating mobile applications as supplementary tools alongside explicit instruction, particularly in resource-variable contexts. Originality – This study contributes a nationwide exploratory perspective on grammar-focused mobile learning among HSC students, emphasizing the intersection of usability, learner autonomy, and infrastructural inequality. It provides direction for future research to examine longitudinal impacts on writing performance and grammar acquisition.
An Adaptive Attention-Based Multimodal Deep Fusion Network for Robust Biometric User Authentication Nurdin; Indo Intan; St. Aminah Dinayati Gani; Nur Salman; Erni Marlina
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.v4i2.448

Abstract

Purpose - The global rise in identity fraud and credential-based breaches, with average costs reaching USD 4.88 million, highlights the limitations of unimodal biometric authentication systems, which are vulnerable to spoofing attacks and environmental degradation. Existing multimodal approaches also rely on static weighting strategies that lack adaptability to adversarial conditions or degraded data quality.This study proposes a Multimodal Deep Fusion Network (MDFN), an end-to-end deep learning architecture that integrates three biometric modalities: facial (visual), voice (audio), and keystroke dynamics (behavioral). The MDFN employs three independent feature extraction streams—ResNet-18 for visual data, 1D CNN for audio data, and Bi-LSTM for behavioral data—fused through an Attention-based Adaptive Weighted Fusion mechanism that dynamically adjusts to data quality. Methods - The evaluation was conducted using the VGGFace2, VoxCeleb1, and CMU Keystroke datasets under both normal and spoofing attack scenarios, using metrics such as EER, FAR, FRR, AUC, and APCER. Findings - The results show that the MDFN achieves an EER of 1.12% and an FAR of 1.12%, significantly outperforming the unimodal baselines (best EER: 4.15%) and static fusion models (EER: 1.95%). The system also demonstrated strong robustness against spoofing, achieving an APCER as low as 0.8%. Research Implications - MDFN is an effective authentication solution for high-security environments. Originality - Its key contribution lies in the attention-based adaptive fusion mechanism, which dynamically adjusts the modality weights based on a real-time quality assessment.
Learning Agility Measurement Models in Technology-Driven Learning Environments: A Systematic Literature Review Nurhayi Musdira; Mukhlisin; Minah Sintian
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.v4i2.472

Abstract

Purpose - This study systematically examines how learning agility has been measured in recent literature by identifying dominant measurement models, dimensional structures, and psychometric approaches across contexts. Methods - This study employed a Systematic Literature Review (SLR) guided by the PRISMA 2020 statement. A structured search was conducted in the Scopus database using predefined keywords. The selection process followed identification, screening, eligibility, and inclusion stages based on criteria such as publication year (2020–2025), document type (peer-reviewed journal articles), language (English), and full-text accessibility. A total of 24 articles were included and appraised using the Mixed Methods Appraisal Tool (MMAT) and analyzed through systematic synthesis. Findings - Learning agility is predominantly measured using self-report Likert-scale instruments, with validation through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The four-dimensional model cognitive, people, change, and results agility remains the most widely used, with variations influenced by context. Analytical techniques are dominated by Structural Equation Modeling (SEM) and Partial Least Squares SEM (PLS-SEM), with learning agility often positioned as a mediating variable. Research Implications - Studies show overreliance on single-source data and limited use of behavioral, longitudinal, and multimethod approaches, indicating the need for more comprehensive, context-sensitive, and technology-integrated measurement models. Originality - This study synthesizes learning agility measurement approaches across contexts, clarifies dominant models, and identifies methodological gaps, highlighting the need for more robust and diversified measurement strategies

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