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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 107 Documents
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Adoption and Utilization of E-Learning Platform Facilities in Higher Education: A Study of the Institut Catholique de Kabgayi (ICK), Rwanda Jean Paul Ndayizigiye
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.v4i2.635

Abstract

E-learning has become an integral part of higher education worldwide, offering new opportunities for flexible, accessible, and student-centered learning. In Sub-Saharan Africa, however, the pace of adoption has been uneven, often constrained by infrastructural and pedagogical challenges. This study investigates the adoption and use of e-learning platforms at the Institut Catholique de Kabgayi (ICK) in Rwanda, with particular attention to institutional readiness, infrastructure, and the perceptions of lecturers and students. Using a mixed-methods design, data were collected from lecturers, administrators, IT staff, and students to assess preparedness, barriers, and opportunities in digital learning. The findings reveal that while ICK has invested in establishing an e-learning platform, its effective use remains limited due to inadequate infrastructure, insufficient training, unstable internet connectivity, and varying attitudes toward online education. Nevertheless, participants acknowledged the potential of e-learning to enhance teaching effectiveness, promote flexibility, and increase access to higher education. The study contributes to the literature by highlighting the specific challenges and opportunities of e-learning adoption in a Rwandan context and offers practical recommendations for policymakers and institutional leaders seeking to strengthen technology-enhanced learning in developing countries.
Classifying Job-Posting Wage Compliance Using Machine Learning: A Comparative Study of Random Forest and Support Vector Machine Algorithms Tiffany Phylicia; Alyssa Christiana Lin; Glaudio Hiewen Tjongdro; Dylan Rael Andrew Bojoh; Evander Banjarnahor
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.v4i2.649

Abstract

Purpose – This study examines wage compliance in online job postings by classifying posted salaries relative to the 2025 DKI Jakarta Provincial Minimum Wage (UMP). It aims to describe below-threshold salary postings and evaluate how salary-range variables affect machine-learning classification. Methods – A raw dataset of 1,221 job postings was collected from Loker.id on October 5, 2025. After removing duplicates, missing values, and salary outliers, the final dataset consisted of 1,143 postings. The target variable was constructed by comparing the mean posted salary range with the UMP threshold. Random Forest and Support Vector Machine (SVM) were evaluated under two scenarios: with and without salary-range variables. Performance was assessed using accuracy, balanced accuracy, F1-score, confusion matrices, and a majority-class baseline. Findings – The descriptive results show that 798 postings, or 69.82%, were classified as Below UMP Jakarta, while 345 postings, or 30.18%, were classified as Meets/Exceeds UMP Jakarta. With salary features included, Random Forest achieved 0.9446 test accuracy and SVM achieved 0.9592. Without salary features, performance declined to 0.7318 for Random Forest and 0.6968 for SVM, with the latter close to the majority-class baseline of 0.6982. Research implications – The findings suggest that the descriptive contribution of this study is stronger than its predictive contribution. Salary-range variables strongly influence classification performance because they are directly related to the construction of the target label. Therefore, machine-learning results should be interpreted cautiously and should not be treated as evidence of robust wage-compliance prediction from broader HR attributes alone. Originality – This study contributes to online labor-market analysis by combining descriptive wage-compliance evidence with an explicit feature-scenario comparison. By evaluating models with and without salary-range variables, the study highlights the importance of addressing threshold-related leakage in job-posting salary classification.
Improving Learner Autonomy and Problem-Solving Skills Through The STAD Cooperative Learning Strategy In Digital Pattern Making for Vocational Fashion Education Nurhijrah; Syarifah Suryana
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.v4i2.650

Abstract

Purpose – The rapid digital transformation of the fashion industry has increased the need for vocational students to develop not only technical competencies but also learner autonomy and problem-solving skills in software-intensive learning environments. This study aimed to investigate the effectiveness of the Student Teams Achievement Divisions (STAD) cooperative learning model integrated with digital pattern-making instruction in improving these competencies among vocational fashion students. Methods – The study employed a quasi-experimental design using a non-equivalent control group pretest–posttest approach. Participants consisted of 59 eleventh-grade students enrolled in a vocational fashion program in Indonesia, divided into an experimental group (n = 30) and a control group (n = 29). Data were collected using a learner autonomy questionnaire and a performance-based problem-solving assessment related to digital pattern-making tasks. Data analysis involved descriptive statistics, paired sample t-tests, independent sample t-tests, and Analysis of Covariance (ANCOVA). Findings – The findings indicated that students who participated in STAD-based digital pattern-making instruction demonstrated higher improvements in learner autonomy and problem-solving skills compared to students who received conventional instruction. ANCOVA results revealed statistically significant treatment effects for learner autonomy and problem-solving skills (p < 0.001). Although large effect sizes were observed, the findings should be interpreted cautiously due to the relatively small sample size, intact-class design, single-institution setting, and partial reliance on self-report measures. Therefore, the study provides preliminary quasi-experimental evidence rather than broad generalizable conclusions. The contribution of this study lies in applying the STAD cooperative learning model to software-intensive vocational fashion learning tasks, particularly digital pattern making. Research implications – The findings suggest that collaborative digital learning environments may support the development of self-regulated learning behaviors and authentic vocational problem-solving skills in fashion education contexts Originality – The contribution of this study lies in applying the STAD cooperative learning model to software-intensive vocational fashion learning tasks, particularly digital pattern making.
Improving the Literal Reading Comprehension Ability of Grade IV Students through the Use of Digital Comic Media in Elementary Schools Vevy Liansari
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.v4i2.695

Abstract

Purpose - This study aimed to analyze the effectiveness of digital comic media in improving the literal reading comprehension skills of fourth-grade elementary school students, particularly in understanding explicit information presented in reading texts.Method - The study employed a quantitative approach with a pre-experimental one-group pretest-posttest design. The participants consisted of 30 fourth-grade students selected using a saturated sampling technique. The research instrument was a 30-item multiple-choice test based on four indicators of literal comprehension: identifying main ideas, locating detailed information, sequencing events, and recognizing cause-and-effect relationships. The instrument was reviewed by two elementary literacy experts and tested for validity and reliability. The intervention was conducted over four learning sessions using digital comics as instructional media in Indonesian language lessons. Data were analyzed using the Shapiro-Wilk normality test and a paired-sample t-test.Findings - The findings revealed a substantial improvement in students’ literal reading comprehension skills. The mean score increased from 45.83 on the pretest to 85.00 on the posttest. The paired-sample t-test showed a statistically significant difference between pretest and posttest scores (p < 0.05), indicating that students’ literal reading comprehension improved after the implementation of digital comic media.Research Implications - These findings suggest that digital comic media can support the improvement of elementary students’ literal reading comprehension skills. However, further studies employing control groups and stronger experimental designs are recommended to provide more robust evidence of its effectiveness.Originality - This study contributes to research on digital learning media by specifically examining the use of digital comics to improve literal reading comprehension among fourth-grade elementary school students through four measured indicators of comprehension.
Reconceptualizing Warehouse Job Profiles through Transversal Skills in a Circular Economy Context Vina Dwiyanti; Ana A; Yusep Sukrawan; Edi Supardi
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.v4i2.707

Abstract

Purpose – This study aims to identify and map Transversal Skills (TS) relevant to Circular Job Profiles (CJP) in the warehousing sector in response to digitalization, automation and Circular Economy (CE) practices. The study addresses the skills gap between industry requirements and workforce competencies in circular warehousing activities such as reverse flows management, material recovery, and resource optimization. Methods – An exploratory qualitative approach was employed through two stages: a Systematic Literature Review (SLR) of 54 scholarly articles and Focus Group Discussions (FGD) involving 12 industry and academic participants. The identified skills were analyzed using thematic analysis and expert validation to develop a competency mapping matrix between TS and CJP. Findings – The study identified six key TS domains that are highly relevant to circular-oriented warehousing roles: communication, teamwork, problem-solving, self-management, digital literacy, and sustainability mindset. The findings indicate that these competencies support the adaptability, flexibility, and sustainability-oriented decision-making required for positions such as warehouse operators, reverse logistics coordinators, and sustainability supervisors. Research implications – This study is limited to qualitative competency mapping and does not measure the direct implementation outcomes of TS in warehousing performance. However, the findings provide a foundation for future quantitative validation and competency framework development. Originality – This study offers a competency mapping framework that aligns TS with CJP in the warehousing sector, contributing to the development of vocational curricula and workforce competency standards for sustainable and technology-driven logistics systems.
Integration of Artificial Intelligence and Blockchain in Inventory Systems for Enhanced Forecasting and Data Security R. Rhoedy Setiawan; Zainur Romadhon
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.v4i2.792

Abstract

Purpose: This study evaluated an inventory system integrating LSTM forecasting and Hyperledger Fabric blockchain to improve prediction accuracy and transaction integrity. Design/Methods: A design-and-development approach used 12,450 inventory records from Retail Company X (January 2021-December 2023), split chronologically into 70% training, 15% validation, and 15% testing subsets. The LSTM used two hidden layers, 128 units per layer, dropout 0.2, Adam optimizer, learning rate 0.001, batch size 64, and 100 epochs. Blockchain used Hyperledger Fabric with Raft consensus. Evaluation included forecasting benchmarks, 50 stock-modification simulations, and 45 purposively recruited users after hands-on prototype interaction. Findings: LSTM achieved MAE 3.2% and RMSE 4.5%, outperforming Moving Average and Exponential Smoothing. A two-tailed paired-samples t-test across 62 matched testing windows against Exponential Smoothing confirmed significant improvement (t(61) = -5.34, p < 0.001, Cohen's dz = 0.68). Blockchain detected 48 of 50 unauthorized stock modifications, producing a 96% detection rate with two missed detections (4%) and 120 ms latency. User evaluation was positive across forecast accuracy, security, transparency, ease of use, and intention to use. Implications: The prototype can support inventory planning, auditability, and secure transaction records. Originality: The study empirically combines AI forecasting, permissioned blockchain integrity, and user acceptance in one inventory workflow.
Development of an IoT-Based Smart Energy System Using Support Vector Machines for Electric Power Consumption Anomaly Detection Dahlan; Irma Eryanti Putri; Rahmat Dani S
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.v4i2.808

Abstract

Purpose - This study aims to develop an Internet of Things (IoT)-based Smart Energy System integrated with the Support Vector Machine (SVM) algorithm for real-time electricity consumption anomaly detection. The study addresses the increasing need for intelligent energy monitoring systems capable of identifying abnormal electricity consumption patterns efficiently and accurately in smart homes and smart building environments. Methods - This research employed a quantitative experimental approach using the Cross Industry Standard Process for Data Mining (CRISP-DM) framework. Electricity consumption data were collected using ESP32-based IoT devices integrated with ACS712 current sensors and ZMPT101B voltage sensors. Data preprocessing included cleaning, normalization using the Min-Max Scaling method, and anomaly labeling based on predefined energy consumption thresholds. The Support Vector Machine algorithm with a Radial Basis Function (RBF) kernel was implemented for anomaly classification. The dataset consisted of 25,000 electricity consumption records collected over 30 days, comprising 81.4% normal data and 18.6% anomaly data. Hyperparameter optimization was performed using Grid Search Cross Validation with 10-fold cross-validation. Findings - The experimental results demonstrate that the proposed SVM model achieved an accuracy of 96.80%, precision of 95.10%, recall of 94.90%, F1-score of 95.00%, and ROC-AUC of 97.20% in detecting electricity consumption anomalies. In addition, the MQTT-based IoT communication system achieved a data transmission success rate of 98.70% with low communication latency. Comparative evaluation results showed that the SVM algorithm outperformed Decision Tree, K-Nearest Neighbor, and Random Forest methods across all evaluation metrics. Implications - The proposed system contributes to the development of intelligent energy monitoring solutions capable of supporting real-time anomaly detection, reducing energy waste, and improving operational efficiency in smart energy environments. The lightweight computational characteristics of SVM also make the proposed approach suitable for IoT devices with limited computational resources. However, further testing using industrial-scale datasets is required to improve system generalization and large-scale deployment capability. Originality - This study presents an integrated smart energy architecture combining IoT-based real-time monitoring, MQTT communication, database systems, and SVM-based anomaly detection within a unified intelligent energy management framework.
Evaluating Ordinal Regression Approaches for Automated Knee Osteoarthritis Severity Classification Using ResNet-18 on Radiographic X-Ray Images Muhammad Kamal Khatami; Fatma Indriani; Andi Farmadi; Muliadi; Muhammad Itqan Mazdadi
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.v4i2.845

Abstract

Purpose – This study examines whether explicitly modeling ordinal severity relationships improves automated Knee Osteoarthritis (KOA) grading compared to conventional multi-class classification. Most existing studies treat KL grade prediction as a categorical problem, overlooking the ordered structure inherent to disease severity progression. Methods – A comparative experimental study was conducted using the publicly available Knee Osteoarthritis (KOA) dataset obtained from Kaggle (Tiwari, 2021). Although the original dataset contains 9,786 knee X-ray images categorized into five Kellgren–Lawrence (KL) severity grades, only the predefined training, validation, and testing subsets were utilized, resulting in a total of 8,260 images for model development and evaluation. Four classification approaches were evaluated under identical experimental conditions: conventional multi-class classification (baseline), Cumulative Link Model (CLM), Binary Cross-Entropy (BCE), and Consistent Rank Logits (CORAL), all using a pretrained ResNet-18 backbone. Performance was assessed using Accuracy, AUC, MAE, Quadratic Weighted Kappa (QWK), and F1-score. Findings – BCE achieved the highest observed performance among the evaluated approaches under the current predefined experimental setting, obtaining 67.81% ACC, 89.72% AUC, 0.3702 MAE, 0.8393 QWK, and 0.6750 F1-score. The baseline model remained competitive with 64.01% ACC and 0.7948 QWK, while CLM produced the lowest QWK (0.7713) and CORAL recorded the lowest F1-score (0.3769). Across all methods, Grade 1 remained the most challenging severity category, indicating persistent difficulty in distinguishing early-stage KOA cases. Research implications – The findings suggest that threshold-based ordinal decomposition may provide a practical balance between ordinal awareness and predictive flexibility for severity grading tasks under the current experimental setting. The study is limited to a single dataset and backbone architecture, and external validation is needed before broader clinical deployment. Originality – This study provides a controlled comparative evaluation of multiple ordinal regression formulations for KOA severity grading, demonstrating that ordinal learning effectiveness depends heavily on how rank relationships are modeled rather than on ordinal awareness alone.
Internet of Things-Integrated Engine Cut-Off System for Monitoring Motorcycle Passenger Load Capacity Using Load Cell Sensors Syahputra Fauzan; Gunawan; Raka Pratindy; Moch. Aziz Kurniawan
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.v4i2.858

Abstract

Purpose – This study aimed to design and develop a motorcycle passenger load capacity monitoring prototype using load cell sensors integrated with an engine cut-off feature. The system was developed to detect loads exceeding the safe capacity threshold and activate a relay-based cut-off response under controlled prototype testing conditions. Methods – This research employed a Research and Development (R&D) approach involving hardware design, component assembly, sensor calibration, software programming, and functional testing. The system integrated a half-bridge load cell sensor, HX711 amplifier, ESP32 microcontroller, 20x4 I2C LCD display, relay module, GPS NEO-6M, step-down voltage converter, and web-based monitoring platform. The prototype was tested on a Honda BeAT 2013 motorcycle with a maximum safe load threshold of 123 kg. Findings – The load cell sensor accurately measured load variations from 40 kg to 140 kg, with readings closely matching reference load values. The engine cut-off system activated reliably when the load exceeded 123 kg for five consecutive seconds, with response times of 5 seconds at 130 kg and 140 kg. LCD and website displays showed synchronized values across all test loads. GPS testing produced an average deviation of 6.86 meters and an average accuracy of 86.29%. Research implications – The prototype demonstrates the technical feasibility of integrating load sensing, engine cut-off control, GPS tracking, and IoT-based monitoring for motorcycle overload prevention under controlled static testing conditions. Further dynamic-road testing, multi-sensor validation, IoT latency evaluation, and user-centered website testing are required before operational deployment. Originality – This study presents an integrated motorcycle safety prototype combining load cell sensing, HX711 amplification, ESP32 control, relay-based engine cut-off, LCD display, GPS tracking, and real-time website monitoring.
Classification of Students' Facial Expressions Utilizing Convolutional Kolmogorov–Arnold Networks (C-KANs) and Classroom Teaching Methods Classification Employing ResNet-152 Hutami Endang; Annahl Riadi; Alif Fauzan; Andi Jamiati Paramita; Furqan Zakiyabarsi; Hany Alexanders
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.v4i2.859

Abstract

Purpose - This study proposes a deep learning parallel dual-classification framework utilizing a lightweight Convolutional Kolmogorov–Arnold Network (C-KAN) to recognize six classes of students' facial expressions and a ResNet-152 to classify three types of teaching methods.Methods - The parallel framework routes micro-student expressions and macro-classroom contexts separately. The C-KAN model features a two-pronged architecture combining 96×96 pixel grayscale facial images with 9 geometric features from 68 landmarks, while ResNet-152 processes 256×256 pixel RGB full-classroom frames. Findings - Evaluated on elementary school recordings (9,130 teaching method images and 2,069 facial expression instances), ResNet-152 achieved 95% accuracy (0.94 macro F1-score). Meanwhile, C-KAN achieved 87% accuracy (0.86 macro F1-score) across six facial expressions.Research implications - By leveraging learnable B-spline functions, C-KAN models complex non-linear micro-expressions accurately with only 4.1 million parameters. This structural efficiency slashes inference time to 11 ms, proving its high viability for real-time edge-computing analytics.Originality - His framework introduces a dual-perspective routing approach. Integrating spline-based C-KAN provides high discriminative power with low computational overhead, supporting evidence-based, automatic evaluation of classroom learning.

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