Journal of Informatics and Vocational Education
Journal Title Journal of Informatics and Vocational Education Initials JOIVE Frequency 3 (Three) Issues Annually: March, July, and November E-ISSN 2746-7813 Chief Editor Dr. Aris Budianto, ST, M.Eng. DOI Prefix 10.20961/joive Publisher Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret Accreditation Sinta 3 Accredited JOIVE is a peer-reviewed journal providing a forum for presenting the latest original research results and developments in the fields of informatics and applied computer science education. The journal promotes discussion among researchers, students, and practitioners in informatics and practical educators both in Indonesia and beyond where a distinctive methodology of teaching and learning informatics has been developed and is of great interest. Important Note: Starting from Volume 7, Issue 2 (2024), JOIVE has upgraded its system from OJS2 to OJS3 (this website). Please note that old accounts on the OJS 2 system are different and cannot be used in the new OJS 3 system. For submission purposes, authors are required to create a new account on the new website. The archives of previous publications using OJS2 (up to Volume 7, Issue 1, 2024) can be accessed through the old website: https://jurnal.uns.ac.id/joive/. INDEXED :
Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret
Sentiment Analysis and Topic Modeling of Ruangguru Application User Reviews Using IndoBERT and BERTopic on a Kaggle Dataset
User reviews provide valuable information for evaluating digital learning platforms because they contain direct expressions of user satisfaction, complaints, and expectations. This study analyzed user reviews of the Ruangguru application using a Kaggle dataset by combining IndoBERT for sentiment classification and BERTopic for topic modeling. The study aimed to classify user sentiment, compare IndoBERT with a TF-IDF and Logistic Regression baseline, identify dominant discussion topics, and examine whether the model performance difference was statistically significant. The dataset originally contained 100,000 reviews, and 76,699 valid reviews were used after data cleaning and preprocessing. Sentiment labels were generated from rating values and grouped into negative, neutral, and positive classes. IndoBERT achieved an accuracy of 0.8920 and an F1 macro score of 0.6347, outperforming the baseline model with an accuracy of 0.8092 and an F1 macro score of 0.5666. McNemar’s test confirmed that the performance difference was statistically significant (chi-square = 594.30, p < 0.001). BERTopic generated 41 topic groups, including one outlier group. Positive sentiment dominated most topics, particularly those related to learning support, material comprehension, and video-based learning. Negative sentiment was concentrated in topics related to payment, application updates, advertisements, and account issues. These findings show that integrating IndoBERT and BERTopic provides a comprehensive understanding of user perceptions toward educational applications.
Copyright (c) 2026 Journal of Informatics and Vocational Education https://creativecommons.org/licenses/by-sa/4.0
One-Hour-Ahead Mean Radiant Temperature Forecasting in Jabodetabek Using CNN-LSTM and Temporal Convolutional Networks
Mean Radiant Temperature (MRT) represents the combined shortwave and longwave radiant load experienced by a human body, but continuous observations are rarely available across large metropolitan areas. This study developed and compared a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model and a Temporal Convolutional Network (TCN) for one-hour-ahead MRT forecasting in Jabodetabek. The dataset comprised 210,528 hourly records from 2023–2024 at 12 ERA5 grid points. Five radiation variables and two near-surface thermal variables were used as predictors, while ERA5-HEAT MRT was the target. Each sample contained a 24-hour historical window. Three chronological train-validation-test splits were evaluated: 80:10:10, 70:10:20, and 70:15:15. Both architectures achieved R² values above 0.93 in all scenarios. Under the 80:10:10 split, TCN produced the lowest RMSE value of 2.8104 °C, the highest R² value of 0.9518, the lowest validation loss value of 5.9592, and a residual bias of −0.4863 °C. CNN-LSTM achieved the lowest MAE value of 1.8874 °C and MAPE value of 5.76% and was more stable when the training proportion decreased. Overall, TCN 80:10:10 was selected as the best configuration, although field validation is required before operational deployment.
Spatiotemporal Dynamics of El Niño Modoki Impacts on East Java Rainfall Using EOF, BIRCH Clustering, and Wavelet Coherence
El Niño Modoki is a variation of El Niño characterized by sea-surface-temperature warming in the central Pacific, flanked by cooling in the eastern and western Pacific, and can influence rainfall patterns in Indonesia, particularly in East Java. This region was selected because it is one of Indonesia’s major food-producing areas, has high rainfall variability, and remains highly vulnerable to drought. This study aimed to analyze the impact of El Niño Modoki on monthly rainfall anomalies in East Java during 1991–2024. The data consisted of the El Niño Modoki Index (EMI) and monthly gridded rainfall data. The analytical methods included rainfall-anomaly calculation, Pearson correlation, Empirical Orthogonal Function (EOF), BIRCH clustering, Wavelet Coherence (WTC), and composite analysis. BIRCH clustering based on the first three EOF modes formed four rainfall-pattern clusters in East Java. WTC analysis showed that the relationship between EMI and rainfall was more dominant at interannual periods of approximately 1–4 years. Composite analysis indicated that El Niño Modoki reduced rainfall in East Java starting from the JJA period and became stronger and more spatially extensive during ASO. Overall, the impact of El Niño Modoki on East Java rainfall was spatial, seasonal, dynamic, and non-homogeneous across regions. These findings provide preliminary information for drought mitigation, water-resource management, and climate early-warning strengthening in East Java.
Predicting Solar Radiation Using Temporal Convolutional Networks and Long Short-Term Memory: A Comparative Study Based on Automatic Weather Station Data
Solar radiation is a key meteorological parameter for climatology, hydrology, agriculture, renewable energy, and environmental analysis. Direct measurement remains limited because it requires specialised instruments, regular calibration, and high maintenance costs. This study compared Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) models for predicting solar radiation using Automatic Weather Station data from the Aceh Climatological Station. The dataset comprised 4,152 observations from January to December 2023, with rainfall, air temperature, relative humidity, air pressure, wind speed, wind direction, and time features as predictors, while solar radiation served as the target variable. The research process included data quality control, interpolation of invalid values, Min-Max Scaling, time feature engineering, sliding-window sequence generation with 24 time steps, and time-based data splitting using 70:15:15, 80:10:10, and 70:10:20 scenarios. Model performance was evaluated using MAE, RMSE, MAPE, and R². The 80:10:10 split produced the best results for both models. The LSTM model achieved slightly better overall performance than TCN based on RMSE and R², although both models captured the temporal pattern of solar radiation. The study provides an AWS-based temporal deep learning framework for radiation prediction in tropical regions of Indonesia.
Performance Evaluation of Support Vector Machine and Naïve Bayes Methods in Mining Sentiment from Shopee Application Reviews
Shopee is one of the most popular e-commerce platforms in Indonesia, with millions of individuals using it to buy and sell online. The number of user reviews on the Shopee app could be an essential metric in terms of the amount of consumer satisfaction with the services provided. The research intends to analyse the sentiment of Shopee’s customer reviews using the Naïve Bayes and Support Vector Machine (SVM) algorithms. In this work, the dataset used is the customer review data from Shopee app retrieved from Kaggle platform with a total of 9,561 reviews. The research process includes data selection, text pre-processing (Case Folding, Cleaning, Tokenising, Stopword Removal, Stemming), sentiment labelling based on rating into positive and negative class, data balancing with Synthetic Minority Over-sampling Technique (SMOTE), feature transformation using Term Frequency- Inverse Document Frequency (TF-IDF) and data splitting into training and testing set with 80:20 ratio. Naïve Bayes and Support Vector machine approaches were used to carry out the classification. Evaluation parameters used were accuracy, precision, recall, F1-score and confusion matrix. The experimental results reveal that the Naive Bayes technique has an accuracy of 89.73%, a precision of 93.92%, a recall of 84.96% and an F1-score of 89.22%. However, SVM approach achieved 90.37% F1 score, 87.27% recall, 90.70% accuracy and 93.70% precision. The assessment result indicates that SVM technique is better than Naïve Bayes strategy in sentiment classification of customer reviews in Shopee. High accuracy and F1 score. The study result shows that the SVM method is better than the Naïve Bayes method in Shopee customer reviews sentiment categorisation.
Sentiment Analysis; Naïve Bayes; Support Vector Machine; TF-IDF; SMOTE.
SQL Island for SQL Learning in Indonesian Vocational Education: A Quasi-Experimental Study of Learning Outcomes and Motivation
Game-based learning (GBL) has gained considerable attention as a strategy to enhance student engagement in technical subjects, yet evidence from vocational secondary school contexts in Southeast Asia remains limited. This study examined the effect of SQL Island — a web-based SQL learning game — on the learning outcomes and motivation of Grade XI vocational students (RPL program) at SMK Negeri 2 Karanganyar, Indonesia. A quasi-experimental Pretest-Posttest Control Group Design was employed with 53 students (n=23 experimental, n=30 control) selected through cluster random sampling. Instruments included a 12-item essay test (ICC=0.985) and a 22-item Likert scale motivation questionnaire (Cronbach's α=0.770), both validated using Aiken's content validity formula. Data were analyzed using ANCOVA, Independent Samples t-test, Mann-Whitney U, and N-Gain in Jamovi. Results showed no significant effect of SQL Island on learning outcomes (ANCOVA: F=0.465, p=0.498, η²p=0.009) or motivation (t=0.030, p=0.976). Post-hoc power analysis revealed the study was substantially underpowered (1−β=0.07), contextualizing the null result. Critically, a ceiling effect in the experimental class (mean pretest=87.2/100) severely constrained measurable improvement, yielding negative mean N-Gain scores for both groups (experimental: −0.441; control: −0.186). These findings contribute methodological insight on ceiling effect as an underreported confound in quasi-experimental GBL research, and offer practical guidance for future implementations: pre-assessment screening, extended treatment duration (≥4 sessions), and language scaffolding are recommended before deploying SQL Island in vocational settings.
Transformation of Meme-Based Learning Assessment: Multimodal Assessment of Generation Z Student Artifacts
Digital transformation has reshaped how Generation Z students communicate knowledge, prompting a re-examination of assessment practices in higher education. Assessment must evolve beyond traditional text-based tasks to accommodate multimodal literacies, yet empirical investigation of rubric-based dimensions in student-created multimodal artifacts remains underexplored. This study analyzes the quality of student meme artifacts as multimodal assessments and examines statistical relationships among assessment dimensions. A quantitative descriptive-correlational design was employed to analyze 155 student-generated meme artifacts. The analysis used a researcher-developed six-dimensional rubric to assess the data. Data were analyzed using descriptive statistics and Pearson correlation via IBM SPSS. Results indicate that overall meme quality falls predominantly in the Very High category across five of six dimensions, with Creativity and Originality as the sole dimension in the High category, with Ethics and Audience Appropriateness (M = 3.99) and Scientific Accuracy (M = 3.81) achieving the highest scores, while Creativity and Originality (M = 3.20) showed the greatest variation. Correlation analysis reveals significant positive relationships among most dimensions (p < 0.01). Multimodal Orchestration emerged as the central dimension, showing the strongest linkage with Creativity and Originality (r = 0.643) and Message Clarity (r = 0.420). These findings support the validity of meme-based assessment and highlight multimodal orchestration as a key factor in artifact quality.
Development of the SOPANSPEAK Interactive Multimedia Application as a Learning Innovation to Instill Language Politeness in Early Childhood
Language politeness is an important aspect in the moral, social, and communication development of early childhood. However, language politeness learning in early childhood education institutions is generally still carried out through habituation and direct examples, so that children do not receive interactive and contextual learning experiences. This study aims to develop an interactive multimedia application, SOPANSPEAK, as a learning medium to instill language politeness in early childhood. The study used the Research and Development (R&D) method with the Lee & Owens model, which includes the stages of analysis, design, development, implementation, and evaluation. The study was conducted at TK Muslimat NU 2 Singosari with research subjects of 15 children in group B3 aged 5–6 years. Data collection techniques included observation, interviews, validation by material experts, validation by media experts, and product trials. The resulting product is an interactive multimedia application that integrates digital stories, educational songs, learning videos, educational games, interactive puzzles, and reward features in one learning medium. The results showed that the SOPANSPEAK application met the eligibility criteria based on expert assessment and user trials. This application can be used as a learning innovation to help children understand and get used to using polite language in everyday life.
The Implementation of Digital Ecosystem in English Education Context as Part of Institutional Strategy: A Case Study in UIN Syahada Padangsidimpuan
The rapid development of digital technology has encouraged higher education institutions to adopt digital transformation strategies to improve institutional performance. However, studies on the role of integrated digital ecosystems in English education performance remained limited. This study aimed to examine how digital ecosystem development functions as an institutional strategy to enhance English education performance at UIN Syahada Padangsidimpuan. A qualitative case study design was employed. Data were collected through interviews, observations, and document analysis, and were analyzed thematically. The findings showed that digital ecosystem development served as an institutional strategy integrating learning processes, academic services, and institutional management. This ecosystem was built through the alignment of technological infrastructure, academic information systems, digital learning platforms, and human resource readiness. These components are interconnected and support institutional effectiveness. The study identified three key contributions to performance: improved learning quality, increased service efficiency, and strengthened data-driven management. This study provided a comprehensive understanding of ecosystem-based digital transformation and highlighted its role in enhancing English education performance.
Development of Interactive E-Modules Based on Blender 3D Visualization: Analysis of Extraneous Cognitive Load and Its Effectiveness on Learning Outcome
The growing demand for technology-based learning media has encouraged the development of interactive e-modules with three-dimensional visualization. However, poorly designed 3D media may increase extraneous cognitive load (ECL) and hinder students’ understanding. This study offers novelty by developing a 3D Blender-based interactive e-module designed with cognitive load management principles, particularly to minimize ECL while improving learning outcomes. The study aimed to develop the e-module and evaluate its impact on students’ cognitive load and achievement. This research used the Research and Development (R&D) method with the ADDIE model. The implementation involved 48 students at the University of Education Indonesia selected through purposive sampling using a one-group pretest-posttest design. Cognitive load was measured using a nine-item psychological scale questionnaire covering ICL, ECL, and GCL, while learning outcomes were assessed using a 15-item multiple-choice test. Data were analyzed descriptively and inferentially using the Shapiro-Wilk normality test and the Wilcoxon Signed Rank Test. The results showed that students’ ECL was low (x̄ = 2.09), indicating that the e-module did not overload working memory. Learning outcomes increased significantly from 34.67% to 84.67% (p = 0.000002). These findings indicate that the proposed e-module effectively supports conceptual understanding while reducing unnecessary cognitive load.
Copyright (c) 2026 Journal of Informatics and Vocational Education https://creativecommons.org/licenses/by-sa/4.0/
Learning Innovation Strategies and Their Role in Promoting Student Active Participation in Informatics Education: A Qualitative Inquiry
Active student participation is a recognized indicator of effective learning, yet passive engagement remains prevalent in Indonesian secondary classrooms, particularly in technology-intensive subjects such as Informatics. This qualitative descriptive study investigated how learning innovations implemented by an Informatics teacher influenced student active participation at a senior high school in Sukoharjo, Central Java, Indonesia. Using purposive sampling, eight participants were selected, comprising seven Grade XI students (six female, one male) and one experienced Informatics teacher. Data was collected through a direct semi-structured teacher interview of approximately 30 minutes, structured written responses from students comprising 13 open-ended questions, and classroom observation. Data was analyzed using thematic analysis. Three key themes emerged from the findings. First, practical and laboratory-based activities were the primary drivers of student engagement, with six of seven students reporting higher motivation during hands-on sessions. Second, teacher instructional methods, particularly interactive, discussion-based, and project-oriented approaches, directly influenced students' willingness to ask questions and contribute to group work. Third, individual interest in Informatics varied considerably, with one student expressing low motivation due to language barriers in English-language materials, highlighting the need for differentiated instruction. These findings suggest that contextually relevant, practice-centered learning innovations are essential for fostering active participation in Informatics education at the senior high school level in Indonesia.
Copyright (c) 2025 Journal of Informatics and Vocational Education https://creativecommons.org/licenses/by-sa/4.0/
Immersive TOEFL Preparation in the Metaverse: Usability and Navigability of a Roblox-Based Game Developed via GDLC
Despite the growing adoption of game-based learning in language education, three-dimensional metaverse platforms for TOEFL preparation remain critically underexplored, leaving learners reliant on drill-based 2D media that lack immersion and sustained engagement. This study addresses that gap by developing a Roblox-based TOEFL learning game using the Game Development Life Cycle (GDLC) and evaluating user understanding of its game flow through graduated formative evaluation. An R&D design was employed, implementing six GDLC stages alongside Tessmer's formative evaluation: one-on-one (n=3), small group (n=5), and field testing (n=40). Data were gathered via observation, interview, and Likert-scale questionnaires, and analyzed descriptively. The game was realized as an area-based environment with three thematic zones and a multi-level navigation system. Evaluations showed progressive quality improvement: one-on-one (M=3.25), small group (M=4.00), and field testing (M=3.84, 96.3% positive response), indicating satisfactory usability and navigability. However, awareness of the Challenge Room and return-route comprehension remain areas requiring refinement. Theoretically, this study demonstrates that GDLC paired with formative evaluation provides a structured and iterative framework for validating metaverse-based educational games. Practically, the findings offer actionable design principles for educators and developers building immersive, game-based language learning environments within social 3D platforms
Face Recognition-Based Attendance System for Village Officials Using YOLOv8n, ArcFace, and Web-Based Microservice ArchitectureFace Recognition-Based Attendance System for Village Officials Using YOLOv8n, ArcFace, and Web-Based Microservice Architecture
The attendance system at Bencah Village has been conducted manually, creating vulnerability to data manipulation, recording errors, and lack of transparency in managing village official attendance data. This study aimed to design and develop a face recognition-based attendance system for Bencah Village officials using deep learning technology, implemented as a web-based platform. The system was developed using the Waterfall Software Development Life Cycle (SDLC) model with a layered microservice architecture, integrating face detection using the lightweight YOLOv8n model, face feature extraction using InsightFace buffalo_l with the ArcFace approach generating 512-dimensional embedding vectors, and identity matching using cosine similarity with a threshold of 0.5. The backend was built with Laravel 12 and Python Flask, containerized using Docker and communicating via REST API. Functional testing using the black-box testing method on 25 main system functions confirmed that all functions performed as expected. Face recognition performance evaluation on 30 test data yielded a Recognition Accuracy of 93.33%, a False Acceptance Rate (FAR) of 0%, and a False Rejection Rate (FRR) of 10%. The FAR of 0% confirmed the system successfully prevented unauthorized identity acceptance, which is critical for attendance data integrity. These results demonstrate the system is feasible as a biometric attendance solution for village government environments.
Attendance system, ArcFace, deep learning, face recognition, YOLOv8n
Early Fire Detection Using IoT and Deep Learning
Fire incidents in strategic facilities such as weapon storage rooms can cause severe damage, threaten personnel safety, and disrupt operational readiness. Conventional fire detection systems generally rely on smoke or temperature sensors, which often respond only after hazardous conditions reach a certain threshold. Therefore, this study proposes an early fire prevention system based on Internet of Things (IoT) and deep learning using CCTV cameras. The system was developed using the System Development Life Cycle (SDLC) with the Waterfall model. The object detection model employed YOLOv8 and was trained on a laptop before being deployed to a Raspberry Pi 5 as the real-time processing unit. The implemented hardware consisted of a Raspberry Pi 5, a Logitech webcam, monitor, keyboard, and mouse. Testing was conducted in a room measuring 6 m × 4 m × 3.5 m. The developed system successfully detected four object classes, namely fire, smoke, cigarette, and person. The implemented logic mechanism classified fire detection as a fire incident, while simultaneous cigarette and smoke detection was categorised as smoking activity with potential fire risk. In addition, the system successfully sent automatic warning notifications through Telegram, enabling faster response without continuous manual monitoring. The results indicate that combining YOLOv8, Raspberry Pi 5, and IoT communication can provide an effective, practical, and low-cost intelligent fire prevention solution for indoor strategic facilities.
Early fire detection, YOLOv8, Internet of Things, Raspberry Pi 5, CCTV monitoring
Machine Learning-Based Early Warning for Student Dropout: Evidence from LMS Behavioral Engagement Patterns in Online Higher Education
Student dropout in online higher education remains critically high, far exceeding face-to-face rates, yet declining behavioral activity in Learning Management Systems (LMS) offers key signals for early intervention. To identify robust predictors and model suitability for Early Warning Systems (EWS), this study presents a comparative analysis of machine learning for dropout prediction using clickstream data from the Open University Learning Analytics Dataset (OULAD), covering 32,593 students across seven undergraduate modules. Three supervised algorithms with Logistic Regression, Random Forest, and Support Vector Machine (SVM), were trained on 13 engineered features combining behavioral and demographic attributes from the Virtual Learning Environment (VLE), with Recall prioritized to minimize missed at-risk students. Results demonstrate that all models achieved strong discriminatory performance with AUC-ROC > 0.93; specifically, SVM provided the highest EWS fit with recall of 0.903, missing only 196 of 2,031 withdrawals (9.7%), while Random Forest attained the best overall accuracy (0.866) and AUC-ROC (0.940). Feature importance analysis further revealed that VLE behavior accounted for 85.0% of predictive power, with Activity Span emerging as the dominant predictor at 41.3%. Cross-module validation confirmed temporal engagement consistency as a robust, generalizable dropout signal. Therefore, these findings provide practical guidance for implementing data-driven EWS in online learning by prioritizing behavioral span metrics over static demographics
Agile-Driven Digital Transformation in Traditional Educational Institutions: Integrating Scrum, Automated Testing, and TAM for Student Registration Systems
Agile methodologies' integration with automated testing and user acceptance evaluation within traditional Islamic boarding school (pesantren) contexts remains understudied. This study develops 'SIAP AMWIN', a web-based student registration system integrating Scrum, Katalon Studio automated testing, and TAM. R&D design was employed across five Scrum cycles (Aug 2023–Feb 2024). A validated 16-item TAM questionnaire (4 Usefulness + 12 Ease of Use items, expert-validated by 2 validators) was administered to 25 new students following system use. Blackbox testing was conducted via Katalon Studio. The system achieved Usefulness 13.96 and Ease of Use 41.44 (overall 86.56%, Very Good), a significant improvement over the prior system score of 12.1 (Not Good). Scrum-driven, actor-centered development effectively enables digital transformation in resource-constrained traditional institutions, providing a replicable blueprint for similar pesantren contexts.
The Influence of Game-Based Learning Assisted by Wordwall on Informatics Learning Outcomes
The research was conducted with the aim of knowing the difference in the effect of the application of game-based learning assisted by Wordwall on the learning outcomes of the informatics class VII SMP Negeri 3 Colomadu. The design of this research is a quasi-experiment. The population used was all seventh-grade students of SMP Negeri 3 Colomadu. The sampling technique used was cluster random sampling, so that the samples used were 32 students from class VII A as the experimental class group and 32 students from class VII B as the control class group. The technique used is by giving tests which include a pretest and a posttest. Prerequisite test by doing the normality test with the Kolmogorov-Smirnov and the homogeneity test with Levene. Data analysis is by independent sample t-test and paired sample t-test with a significant value limit of 0.05 and N-Gain Score, with the help of the SPSS 25 application program. The results obtained show that there is an influence on the application of game- based learning aided by Wordwall on the learning outcomes of the informatics class VII SMP Negeri 3 Colomadu. It can be seen from the calculation of the independent sample t-test, which shows a significance value of 0.036 <0.05, and the paired sample t-test results show a value of 0.000 <0.05. These results show that the use of game-based learning assisted by Wordwall has an effect on learning outcomes. The results on the N-Gain Score also show that the score obtained by the experimental group is higher than the control group, namely 0.4212 > 0.1630. From these results, the experimental group obtained a score that is moderate. These findings suggest that digitally gamified review platforms can serve as effective pedagogical supplements in technology-integrated classrooms, warranting further investigation across diverse subject areas and school contexts.
The Effectiveness of Fast- vs. Slow-Tempo Music on Students’ Cognitive Performance: A Within-Subject Experimental Design
This study examined the effects of music tempo on students’ cognitive performance under three conditions: fast-tempo, slow-tempo, and no music. Despite widespread use of music during studying, it remains empirically unclear whether and how music tempo differentially affects cognitive performance among junior high school students in Indonesia. A quantitative approach with a within-subject repeated measures experimental design was employed, involving 34 ninth-grade students from a junior high school in Indonesia. Each participant completed mathematical problem-solving tasks under three controlled conditions: fast-tempo instrumental music (120–190 BPM), slow-tempo instrumental music (60–80 BPM), and silence. Cognitive performance was measured using accuracy scores, and subjective cognitive load was assessed through the NASA-TLX. Data were analyzed using Repeated Measures ANOVA and validated with the Friedman test due to partial violations of normality assumptions. The results indicated that the fast-tempo condition produced the highest mean accuracy, followed by no music and slow-tempo music. However, the differences were not statistically significant, although a moderate effect size suggested practical relevance. Pairwise comparisons revealed a consistent trend favoring fast-tempo music over slow-tempo and no-music conditions. Notably, NASA-TLX scores indicated that the fast-tempo condition produced significantly lower perceived cognitive load (M = 50.07) compared to slow-tempo (M = 61.59), χ²(2) = 13.41, p = .001, suggesting that fast-tempo music reduced subjective mental effort even when accuracy gains were not statistically significant. These findings support the theoretical perspectives of Cognitive Load Theory and arousal-mood theory, indicating that optimal levels of auditory stimulation may enhance cognitive processing efficiency. The results highlight the practical relevance of fast-tempo music in academic settings and underscore the need for further research with larger samples and physiological measures.
Developing a Bilingual Digital Story to Promote Honesty and Environmental Awareness in Natural Tourism Settings
Improving reading skills in junior high school students is a challenge due to low motivation and a lack of engaging materials. This design and development research (DDR) project aimed to address this by developing a digital bilingual storybook for junior high school students. The storybook features illustrations, moral values, and settings based on local nature tourism. The study utilized the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model to guide the creation process. Data were collected through teacher interviews, students' questionnaires, expert validation, and product testing. The findings indicated that the developed storybook is highly feasible and received positive feedback from both students and teachers. This storybook not only helps students comprehend bilingual texts but also boosts their motivation and builds character through a connection to their local culture. The result suggests that this material is a valuable tool for enhancing reading skills and instilling positive values in students.
Analysis of Android Applications on Google Play Store as a Learning Media for Java Programming Language
Despite the proliferation of Java programming learning applications on mobile platforms, no systematic quality-based framework has been applied to evaluate their instructional adequacy, leaving educators without reliable evidence for media selection. This study aims to systematically evaluate Java programming learning applications available on the Google Play Store to identify those most suitable for use as instructional media. A descriptive qualitative approach with structured assessment was employed, in which applications were systematically collected using predefined keywords and filtered through a six-stage elimination process prior to evaluation. The remaining 30 applications were assessed using the Learning Object Review Instrument (LORI), which evaluates seven aspects: content quality, learning objective alignment, feedback and adaptation, motivation, presentation design, interaction usability, and accessibility. Each aspect was scored on a scale of 1 to 4, and a final percentage score was calculated for each application. Of the 30 applications assessed, three achieved the highest scores: CodeGym (92), Java by Coding and Programming (90), and Java X (89), all of which demonstrated strong performance across content quality and usability dimensions. Notably, Google Play Store user ratings were found to be inversely proportional to LORI-based instructional quality scores, suggesting that popularity metrics do not reflect pedagogical value. These findings indicate that a structured, criteria-based evaluation framework is essential for educators selecting Java learning applications, and that Play Store ratings alone are insufficient as a proxy for instructional quality. Future studies are recommended to validate these findings through user testing with actual learners across multiple educational contexts.
Development of Interactive Learning Media for Vocational High Schools in Basic Computer Network and Telecommunication Engineering Subjects
The development of technology in the Industry 4.0 era has brought significant changes in many aspects of life, including our education. In the formal education system, teachers play a crucial role in creating an engaging and effective learning environment. This research aims to develop an interactive learning media called Comneter (Computer Network Learning) as a learning media to help students with their understanding of basic computer networks and telecommunication in vocational high schools. Comneter was developed using Unity software, a cross-platform game engine that enables us to create apps for both mobile and desktop platforms. The method used for this research is the Research and Development (R&D) method, with ADDIE (Dick et al., 1996) as the model of this development. This model consists of 5 main phases, including analysis, design, development, implementation, and evaluation. The results from this research show that Comneter received a feasibility assessment from a material expert, a media expert, students, and a teacher (as a respondent), with an average score of 3.96, categorizing Comneter as ‘Good’ or feasible. Therefore, it can be concluded that, based on this research, using computers can help enhance students’ comprehension in basic computer network and telecommunications engineering subjects, and has the potential to be used in vocational high schools to create a more effective and engaging learning experience
An Experimental Study on the Comparison of Learning Outcomes in VLAN Configuration Using Graphical Network Simulator 3 and Cisco Packet Tracer
Selecting an appropriate network simulator for advanced VLAN instruction in higher education remains an unresolved pedagogical challenge, as empirical comparisons of GNS3 and Cisco Packet Tracer at university level are scarce. This study aimed to examine (1) whether a significant difference in VLAN configuration learning outcomes exists between students using GNS3 and those using Cisco Packet Tracer, and (2) the relative effectiveness of each simulator for students of the Informatics and Computer Engineering Education Program (PTIK) at Universitas Sebelas Maret. A quasi-experimental pre-test–post-test design was employed with two intact classes (N = 30) from the 2023 cohort, randomly assigned to the GNS3 condition (n = 15) and the Cisco Packet Tracer condition (n = 15). Learning outcomes were measured using ten validated essay items (α = 0.673) and analysed via an independent-samples t-test and normalised gain (N-gain) index. Results revealed that GNS3 users outperformed Packet Tracer users on the post-test (mean 79.33 vs. 72.80), with a statistically significant difference (t (28) = 2.224, p = 0.034). N-gain analysis further showed that the GNS3 group achieved moderate learning effectiveness (g = 0.452), compared to the low category recorded for the Cisco Packet Tracer group (g = 0.277). These findings suggest that GNS3’s authentic Cisco IOS emulation fosters deeper cognitive engagement with advanced VLAN tasks—including inter-VLAN routing and native CLI syntax—than the abstracted environment of Packet Tracer. GNS3 is therefore recommended as the primary simulator for advanced networking courses in higher education, while Packet Tracer remains appropriate for introductory-level instruction. Future research should replicate this design with larger, multi-institutional samples to strengthen external validity.
Enhancing Learning Outcomes in Vocational Animation Education: A Quasi-Experimental Study of Kahoot-Assisted Game-Based Learning in Indonesia
Student disengagement during theory-based instruction in vocational high schools remains a critical pedagogical challenge. Game-based learning (GBL) has emerged as an innovative approach to improve student engagement and achievement, particularly in vocational education. This study aimed to examine the difference in learning outcomes and the effectiveness of the Kahoot-assisted GBL model compared to conventional learning in the 2D Animation subject. A quasi-experimental pretest-posttest control group design was employed. Sixty-one grade XI Multimedia students at a vocational high school in Surakarta were selected through cluster random sampling (experimental n=30, control n=31). Data were collected via validated achievement tests and analyzed using an independent sample t-test and normalized gain index. Results showed a significant difference in learning outcomes (t=5.548, p=0.000<0.05). The normalized gain score of the experimental class (g=0.52, medium category) was significantly higher than the control class (g=0.20, low category). Kahoot-assisted GBL is more effective than conventional learning in improving student learning outcomes in 2D Animation. These findings offer evidence-based guidance for vocational educators seeking to enhance theory-based learning through gamified digital tools. This study contributes to the limited body of evidence on gamified learning in vocational multimedia education within the developing country context.
Learning Barriers in Web Programming Subjects Among Vocational Software Engineering Students: A Qualitative Case Study
This study examines the learning difficulties experienced by vocational high school (SMK) students in the Computer and Network Engineering (TKJ) program in learning web programming. A qualitative case study approach was employed, involving semi-structured interviews, observations, and documentation with 21 students from SMK Negeri 2 Buduran. Data were analyzed using thematic analysis supported by descriptive questionnaire results. The findings show that all students perceived web programming as challenging, with 71% rating it as very difficult and 29% as moderately difficult, while none considered it easy. In terms of learning enjoyment, 76% of students reported a neutral level of enjoyment, indicating limited intrinsic motivation. The analysis revealed several dominant learning barriers, including difficulties with programming syntax and logic, challenges in using development tools such as VSCode and terminal environments, limited access to clear learning resources, and insufficient instructional guidance. Emotional factors, such as frustration and stress caused by repeated errors, further hindered the learning process. Students also identified programming, networking (e.g., TCP/IP and server administration), and microcontroller-related subjects as the most difficult areas within the curriculum. These findings indicate that students’ learning difficulties in web programming are not only technical but also instructional and affective in nature. Therefore, this study emphasizes the need for more interactive, supportive, and student-centered learning strategies. The adoption of project-based learning, enhanced digital learning resources, and guided mentorship is recommended to improve students’ competencies, motivation, and confidence in web programming. The results provide valuable insights for educators, curriculum developers, and policymakers in strengthening vocational education to better meet industry demands.
Instructors’ Acceptance of Roblox Studio for Informatics Education: An Extended TAM with Perceived Risk
The increasing integration of immersive digital platforms in education has created new opportunities for game-based learning; however, tutors’ acceptance of game development platforms remains insufficiently explored in empirical research. This study investigates the factors influencing tutors’ intention to adopt Roblox Studio as a learning platform in informatics education by applying an Extended Technology Acceptance Model (TAM) integrated with perceived risk. Data were collected from 125 informatics tutors in Indonesia using a structured questionnaire and analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with a Robust Maximum Likelihood estimator. The results indicate that the proposed model explains 84.3% of the variance in behavioral intention to use Roblox Studio. Perceived Ease of Use significantly influences Perceived Usefulness, while Attitude Toward Using emerges as the strongest predictor of tutors’ adoption intention. Interestingly, Perceived Ease of Use and Perceived Usefulness do not significantly affect tutors’ attitudes, suggesting that pedagogical considerations may play a more dominant role than purely technical factors in technology adoption decisions. Furthermore, perceived risk does not significantly reduce adoption intention, indicating that tutors tend to prioritize the pedagogical benefits of immersive platforms over potential technological concerns. This study contributes to the literature by extending the Technology Acceptance Model within the context of game-development platforms and providing empirical insights into tutor perspectives in game-based learning adoption. The findings offer practical implications for educators and institutions seeking to integrate immersive digital platforms into informatics education.
Enhancing Students’ Motivation in Algorithm Learning through Web-Based Visual Diagramming Tools: An ARCS Model Analysis
Learning algorithms and flowchart design in secondary informatics education often lack engaging instructional strategies that effectively foster students’ learning motivation. However, limited studies have examined how web-based visual diagramming tools influence students’ motivational dimensions in algorithm learning within the ARCS framework. This study provides empirical evidence on how web-based diagramming tools enhance motivational engagement in algorithm learning. A qualitative descriptive design was employed involving 36 tenth-grade students. The findings reveal that visual interaction through draw.io significantly increased students’ attention and engagement, improved contextual relevance of learning tasks, strengthened learners’ confidence in constructing flowcharts independently, and generated positive learning satisfaction. These findings extend the application of the ARCS model within web-based informatics instruction and highlight the potential of digital visual tools to enhance affective learning outcomes. Integrating digital visual media into informatics instruction can serve as an effective strategy to enhance students’ motivation and engagement in computational learning.
Human–GenAI Score Alignment in Rubric-Constrained Essay Assessment: Procedural Convergence Without Pedagogical Equivalence
The increasing availability of Generative Artificial Intelligence (GenAI) systems has raised critical questions regarding their role in educational assessment, particularly in evaluating open-ended student responses that traditionally rely on professional teacher judgment. This study investigates the extent to which GenAI-generated scores align with teacher assessments when both are guided by an explicit and standardized rubric framework. Using a parallel scoring design, written responses from junior secondary school students were independently evaluated by an Informatics teacher and a GenAI system (ChatGPT) based on an identical rubric encompassing progressively increasing cognitive demands from lower-order to higher-order thinking skills. To examine scoring alignment, an Intraclass Correlation Coefficient (ICC) analysis with a two-way mixed-effects model and absolute agreement approach was employed. The results indicate a meaningful level of score alignment between teacher and GenAI assessments, suggesting that GenAI can apply rubric-based evaluative criteria in a procedurally consistent manner. However, qualitative analysis of written feedback reveals substantive differences in pedagogical depth. While GenAI feedback demonstrates high structural consistency and transparent rubric justification, teacher feedback exhibits greater contextual sensitivity, incorporating instructional intent, student misconceptions, and classroom dynamics. These findings suggest that GenAI systems hold potential as assessment support tools capable of enhancing scoring consistency and efficiency in formative assessment contexts. Nevertheless, score alignment should not be interpreted as pedagogical equivalence. The study concludes that GenAI is best positioned as an augmentative decision-support system operating under teacher supervision, rather than as an autonomous assessor. This research contributes empirical evidence to ongoing discussions on Human–AI score alignment and highlights the critical role of rubric design in mediating responsible GenAI integration within educational assessment practices.
Analysis of Social-Emotional Development Through Collaborative Learning of Informatics Subjects for High School Students
This study aims to analyze the implementation of collaborative learning based on crossword puzzles and its relationship to the social-emotional development of class X E5 students. This study uses a qualitative descriptive approach and data collection through observation and documentation during the learning process. This study shows that the implementation of collaborative learning strategies based on crossword puzzles can increase learning interactions actively and optimally. This is seen from the increase in empathy, self-confidence, communication, interactivity, and the ability to work together in groups. This study shows that the integration of collaborative learning with crossword puzzles not only supports the understanding of computer network concepts but also plays a role in the social-emotional development of students. Thus, collaborative learning based on crossword puzzles can be an innovative learning strategy in informatics subjects that is holistically oriented.
Integration of Artificial Intelligence Models into a Flask-Based Web Application for Classification and Generation of Batik Cual Motifs from Bangka Belitung
Artificial Intelligence (AI) has been increasingly adopted to support digital preservation and creative exploration of cultural heritage. However, many existing studies primarily focus on model development, while limited attention is given to the practical integration and deployment of AI models in real-world applications. This study presents the integration of AI models into a web-based application using the Flask framework for Batik Cual motif classification and generation. The proposed system enables users to interact with AI functionalities through a browser-based interface, supporting motif classification from uploaded images and motif generation based on textual input and reference images. The integration is implemented using a RESTful backend architecture, where Flask manages request handling, preprocessing, AI inference workflows, postprocessing, and result visualization. Functional testing results indicate that the system operates according to its intended design, providing structured outputs with acceptable response times. The findings demonstrate that Flask is an effective and practical framework for deploying AI-driven web applications, particularly for prototype-scale systems aimed at supporting digital preservation and dissemination of traditional cultural motifs.
Generation of Batik Cual Bangka Belitung Motif Variations Using Stable Diffusion Models
Batik Cual Bangka Belitung is a visual cultural heritage characterized by repetitive motifs, fine line details, and high ornament complexity, presenting significant challenges for digital exploration without compromising its visual identity. This study aims to generate variations of Batik Cual motifs using a prompt-guided image-to-image (img2img) approach based on the Stable Diffusion model with a single-reference image. Furthermore, the research analyzes the influence of the strength parameter on the delicate balance between structural similarity and generative visual variation. The dataset consists of 13 independently collected Batik Cual motifs, pre-processed through RGB conversion and standardized to a resolution of 512×512 pixels. Controlled experiments were conducted using varied strength values of 0.4, 0.6, and 0.8, while maintaining other parameters constant. Quantitative evaluation utilized the Structural Similarity Index Measure (SSIM) to assess structural integrity and CLIP similarity to measure semantic alignment between the prompt and output image. The results indicate that increasing strength consistently decreases SSIM values, signifying greater structural deviation from the reference image, whereas CLIP similarity remains relatively stable across configurations. Quantitatively, a strength of 0.4 offers the optimal combination of structural similarity and semantic suitability. However, qualitative assessments reveal that for certain motifs, a strength of 0.6 produces a more balanced variation between pattern innovation and motif character preservation. These findings confirm a measurable trade-off between identity preservation and generative exploration, demonstrating the potential of Stable Diffusion as a controlled method for developing Batik Cual digital assets.