Journal of Informatics and Vocational Education
The Journal of Informatics and Vocational Education (JOIVE) is committed to advancing the understanding of applied computer science education, with a particular focus on the integration of informatics in vocational training and the development of innovative teaching and learning methodologies. Pertinent but not limited to the teaching and learning of informatics, including curriculum design, instructional methods, and the use of technology to enhance the educational experience.Vocational Education,Innovative Educational Practices, Educational Technology Development, Impact of Informatics on Society , and Case Studies and Best Practices. JOIVE scope cover all aspect of Informatics Theory, Application, and Vocational Education, including(but not limited): Informatics Theory, Information System, Mobile and Wireless Communication Computer Networks. Distributed System, Cloud Computing, IoT Data Mining, Artificial intelligence, Machine Learning, and Education Learning Technology E-Learning Educational Technology Vocational Education Emerging technologies in education
Articles
51 Documents
<![CDATA[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 ]]>
<![CDATA[Wenni Siswanti]]>;
<![CDATA[Zikri Wahyuzi]]>;
<![CDATA[Arvi Pramudyantoro]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3516
<![CDATA[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.]]>
<![CDATA[SQL Island for SQL Learning in Indonesian Vocational Education: A Quasi-Experimental Study of Learning Outcomes and Motivation ]]>
<![CDATA[Anis Zahro Dwi Lutfiana]]>;
<![CDATA[Aris Budianto]]>;
<![CDATA[Dwi Maryono]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3645
<![CDATA[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.]]>
<![CDATA[The Implementation of Digital Ecosystem in English Education Context as Part of Institutional Strategy: A Case Study in UIN Syahada Padangsidimpuan ]]>
Hamka, Hamka; Najiah, Nurun
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3676
<![CDATA[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.]]>
<![CDATA[Development of the SOPANSPEAK Interactive Multimedia Application as a Learning Innovation to Instill Language Politeness in Early Childhood ]]>
Khusniyah, Syarifatul; Santoso, Sandy Tegariyani Putri; Mukhlis, Akhmad; Rochmah, Ainur
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3682
<![CDATA[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.]]>
<![CDATA[Development and Feasibility Testing of a Desktop-Based Learning Management System (LMS) Application Using the ADDIE Model ]]>
<![CDATA[Nofrendy Azin]]>;
<![CDATA[Nuur Wachid Abdul Majid]]>;
<![CDATA[Rhezwan Dhaifullah Romdhoni]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3683
<![CDATA[Web-based Learning Management System (LMS) platforms face persistent limitations in Indonesia, particularly complete dependence on internet connectivity, which disrupts learning continuity for students in areas with inadequate digital infrastructure. This study aimed to develop and test the feasibility of a desktop-based LMS application, named EduDesk LMS, using the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model. The application was built using the Tauri framework with Rust as the backend and React for the frontend, integrated with Supabase as the cloud database and authentication service. A key feature was an offline-online dual-mode mechanism, which allowed data to be stored locally when connectivity was unavailable and synchronized to the cloud automatically upon reconnection. The research subjects consisted of two educational technology experts for validation and 50 students selected through purposive sampling for the user trial. Data were collected using a Likert-scale questionnaire covering five assessment aspects: Display and Design, Ease of Use, Offline Feature, Usefulness, and User Satisfaction. Results showed that EduDesk LMS achieved a grand mean of 4.00, placing it in the Feasible category (3.41–4.20) across all five dimensions. The Display and Design aspect obtained the highest mean (4.09), followed by Usefulness (4.04), Ease of Use (4.03), User Satisfaction (3.94), and the Offline Feature (3.90). All aspects exceeded the minimum feasibility threshold of 3.41. These findings demonstrated that the ADDIE model effectively guided the development of a systematic, user-centered desktop LMS, and that EduDesk LMS constitutes a feasible platform for supporting flexible learning in higher education environments with varying connectivity conditions.]]>
<![CDATA[Spatiotemporal Dynamics of El Niño Modoki Impacts on East Java Rainfall Using EOF, BIRCH Clustering, and Wavelet Coherence ]]>
<![CDATA[Diah Ariefianty]]>;
<![CDATA[Agung Budi Susanto]]>;
<![CDATA[Arya Adhyaksa Waskita]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3782
<![CDATA[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.]]>
<![CDATA[Performance Evaluation of Support Vector Machine and Naïve Bayes Methods in Mining Sentiment from Shopee Application Reviews ]]>
<![CDATA[Dandi Azaidane]]>;
<![CDATA[Muhammad Masrur Aji Dorojatun]]>;
<![CDATA[Bisma Satrio Bimantoro]]>;
<![CDATA[Anggraini Puspita Sari]]>;
<![CDATA[Addien Haniefardy]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3741
<![CDATA[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.]]>
<![CDATA[Transformation of Meme-Based Learning Assessment: Multimodal Assessment of Generation Z Student Artifacts ]]>
<![CDATA[Fina Rifana]]>;
<![CDATA[Rizki Hikmawan]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3691
<![CDATA[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.]]>
<![CDATA[One-Hour-Ahead Mean Radiant Temperature Forecasting in Jabodetabek Using CNN-LSTM and Temporal Convolutional Networks ]]>
<![CDATA[Novana Sari]]>;
<![CDATA[Tukiyat Tukiyat]]>;
<![CDATA[Yan Mitha Djaksana]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3802
<![CDATA[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.]]>
<![CDATA[Sentiment Analysis and Topic Modeling of Ruangguru Application User Reviews Using IndoBERT and BERTopic on a Kaggle Dataset ]]>
<![CDATA[Muhammad Fajar Ramadhan]]>;
<![CDATA[Febrianti Panjaitan]]>;
<![CDATA[Winarnie Panjaitan]]>;
<![CDATA[Hery Oktafiandy Panjaitan]]>;
<![CDATA[Yohanes Panjaitan]]>
<![CDATA[ Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026 ]]>
Publisher : <![CDATA[Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret ]]>
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DOI: 10.20961/joive.v9i3.3820
<![CDATA[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.]]>
