Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Filter Icon

Filters article

Years

FAQ Arrow
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

The impact of digital comics as a teaching and learning tool in economics education on future-ready talent development: the mediating role of attitude

10.11591/ijere.v15i5.37622
Hainnuraqma Rahim , Abd Hadi Mustaffa , Norashida Othman , Putri Aliah Mohd Hidzir
Economics education at the tertiary level dominated by text-heavy materials that often disengage students. At the same time, the potential of digital comics as an innovative pedagogical alternative are insufficiently examine. Hence, this study examines the impact of digital comics (IMP) on students’ learning in economic subjects at Universiti Teknologi MARA (UiTM) Melaka, with particular focus on the mediating role of attitude (ATT). A quantitative design was employed using structured questionnaires distributed to 146 diploma and degree students to measure the determinants of perceived IMP. Data were analyzed using the partial least squares– structural equation modeling (PLS-SEM). The findings show that ATT and subjective norms (SN) significantly influence the IMP, while digital literacy (DL) and perceived behavioral control (PBC) exhibit no direct effects. In addition, ATT significantly mediates the relationships with SN, PBC and the IMP. Practically, the findings suggest that lecturers can embed digital comics into case-based class activities to enhance engagement and conceptual understanding. The novelty of the study indicated that this is the first studies applying theory of planned behavior (TPB) to digital comics in economic education. This study also offers future research which can be extend the studies into experimental, longitudinal and cross-sectional.
Volume: 15
Issue: 5
Page: 4192-4206
Publish at: 2026-10-01

Transformational leadership and crisis resilience in K-12 schools: a review

10.11591/ijere.v15i5.40085
Rayner Robert Kenneth , Aida Hanim A. Hamid , Mohamed Yusoff Mohd Nor
Global disruptions, particularly the COVID-19 pandemic, have intensified the need to understand how school leadership supports crisis response and organizational resilience. Although transformational leadership (TL) is widely associated with positive educational outcomes, it is role in crisis contexts remains fragmented and requires clearer synthesis. This systematic literature review examined the relationship between TL, crisis response, and organizational resilience in K–12 schools. Guided by the PRISMA framework, searches were conducted in Scopus, Web of Science, and ERIC. A total of 251 records were identified, of which 21 empirical studies met the final inclusion criteria. Methodological quality was assessed using the mixed methods appraisal tool (2018), and the findings were synthesized thematically. The review found that TL supports crisis response by strengthening direction, trust, communication, teacher commitment, and collective mobilization. However, it is influence is neither direct nor sufficient on its own. Rather, TL contributes to crisis resilience through psychological, organizational, and digital pathways, including teacher self-efficacy, emotional well-being, collective efficacy, organizational learning, relational coordination, and digital adaptation. The findings further indicate that leadership effectiveness is shaped by contextual conditions, including digital infrastructure, workload, organizational support, policy flexibility, and socio-cultural context. This review contributes a critical, mechanism-based explanation of how and under what conditions TL supports crisis resilience in K–12 schools.
Volume: 15
Issue: 5
Page: 3892-3911
Publish at: 2026-10-01

Developing communicative and intercultural competence through integrating art in language teaching

10.11591/ijere.v15i5.39794
Zhanel Mangazina , Shynar Tazhibayeva , Magira Moldabayeva , Ardak Nurgaliyeva , Aliya Zhakupova
The article explores the development of communicative and intercultural communicative competencies in language education with the help of the integration of artistic practices. In the context of the sociocultural theory of language education, the development of communicative competence is an operational language construct, while the development of intercultural communicative competence is a meta-construct that regulates language use. The integration of art is understood not as a supplementary teaching tool, but as a pedagogical mediator that transforms learning processes through experiential engagement, symbolic interpretation, emotional involvement, and reflective dialogue. The article is based on a mixed-methods approach that incorporates a quasi-experiment, control group, and experimental group of students. The results of the study suggest that there is a correlation between the art-based instruction and an increase in communicative activity, intercultural competence, and students’ own reflective capacity. However, the results are seen as indicative rather than causal. The results of the study support the pedagogical value of instruction that is oriented towards art as a means of creating a culturally rich and dialogic learning environment. However, the results also highlight the need for future research into how different art forms influence the development of competence.
Volume: 15
Issue: 5
Page: 4614-4624
Publish at: 2026-10-01

Web traffic optimization and institutional branding in higher education

10.11591/ijere.v15i5.40342
Mohammad Syahidul Haq , Ahmad Abdullah Zawawi , Muhamad Sholeh , Mufarrihul Hazin , Muhammad Ivan Dwi Saputra
Digital visibility has become an important component of institutional branding in higher education, yet limited empirical evidence explains how faculty-level website optimization contributes to brand image formation. This study evaluates web traffic optimization as a strategic capability using the context, input, process, output, and outcome (CIPOO) framework. An evaluative quantitative design was applied at Universitas Negeri Surabaya, involving 341 respondents consisting of 11 faculty website administrators and 330 website users. Data were collected through a five-point Likert-scale questionnaire and analyzed using descriptive evaluation and exploratory Pearson correlation based on aggregated faculty-level scores. The results show that context and outcome achieved the highest evaluation scores, indicating strong institutional alignment and positive branding outcomes, while process showed greater variability across faculties. Correlation analysis revealed a very strong significant positive association between output and outcome, whereas the input–process and process–output relationships were positive but not statistically significant. The novelty of this study lies in integrating faculty-level web traffic optimization, digital governance, and institutional branding within a single CIPOO-based evaluation model. These findings suggest that universities should strengthen website governance, digital capacity, and traffic monitoring to support institutional brand image.
Volume: 15
Issue: 5
Page: 3882-3891
Publish at: 2026-10-01

Evaluating the effectiveness, structure, and influencing factors of legal awareness education activities in higher education institutions

10.11591/ijere.v15i5.39776
Hiep Ho Duc , Long Nguyen Trong , Van Vu Hong
This study evaluates the effectiveness, structure, and influencing factors of legal awareness education activities in higher education institutions in Vietnam. Using a convergent mixed-methods design, the research surveyed 375 administrators, lecturers, and students and conducted 15 semi-structured interviews to integrate statistical evidence with contextual interpretation. The results show that structural components were rated fairly high, but experiential methods and updated legal content remained weaker than lecturer competence and content relevance. Legal attitude received the highest outcome score, whereas lawful behavior, particularly in cyberspace, was less consistent. Reliability, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) results confirmed acceptable measurement quality, and regression analysis explained 46.2% of the variance in legal awareness outcomes. Educational content and methods were the strongest predictors, followed by environment and educational actors. The study contributes a systemic evaluation framework and highlights the awareness-attitude-behavior gap in digital legal education.
Volume: 15
Issue: 5
Page: 3667-3676
Publish at: 2026-10-01

Scaling of sustainable development goals in science education using Thurstone’s pairwise comparison–based scaling technique

10.11591/ijere.v15i5.39770
Ahmet Volkan Yüzüak , Zekiye Merve Öcal , Emrah Hiğde
A lack of evidence-based guidelines on which sustainable development goals (SDGs) need to be focused on in educational settings can be regarded as the key deficiency that leads to an inefficient sustainability learning process. As sustainability-related issues become ever more acute, there is a necessity to determine which SDGs should be prioritized pedagogically. Thus, the current study aims at examining how future teachers evaluate the significance of selected SDGs in relation to environmental learning. Applying the paired comparison methodology, the authors gathered data from 129 third- and fourth-year prospective teachers concerning their perceptions of 11 SDGs (SDG 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, and 15). According to the results, SDG 3, SDG 6, and SDG 2 seem to be the most important goals, while SDG 15, SDG 5, and SDG 14 appear less critical. Thus, one may hypothesize about hierarchy within SDGs based on their importance as prerequisites for environmental problems solving. In terms of practical application, this paper offers a definite methodology that instructors can apply to rank and arrange SDGs for teaching purposes, as well as useful tips for curriculum designers and policymakers who seek to develop sustainability education programs.
Volume: 15
Issue: 5
Page: 3716-3725
Publish at: 2026-10-01

Futures thinking framework: relevance to veterinary students

10.11591/ijere.v15i5.39434
Adrian P. Ybañez , Naomi A. Bajao , Siegfried D. Mendoza , Marlou C. Godinez , Dawn I. Calibo-Senit , Ryan O. Tayco , Vernadyn A. Morillo , Eloiza May S. Galon
The futures thinking framework (FTF) is increasingly recognized as an educational approach that helps learners anticipate change and prepare for long-term challenges, but evidence from Philippine veterinary education remains limited. This cross-sectional study examined how selected Filipino veterinary students perceived the relevance of FTF to career preparedness, organizational learning, and professional adaptability. Following an introductory lecture at the 2024 National Congress of Veterinary Medicine Students, an online survey was administered that included demographic items, 18 five-point perception statements (Cronbach’s α: 0.95), and three open-ended questions. Of 204 responses from 12 veterinary schools, 195 were retained after data cleaning. Data were analyzed using descriptive statistics, Kruskal-Wallis tests, exploratory factor analysis (EFA), internal consistency analysis, and thematic analysis. Most respondents were female (61.5%) and in the 5th–6th year (51.8%). The mean perception score was 4.04/5.00, indicating generally favorable views, particularly regarding eagerness to learn more about FTF and the relevance of student organizations to future professional life. After Bonferroni adjustment, only school differences in perceived administrative support for national organization activities remained significant (p=0.0012). The EFA identified three related domains with good-to-excellent reliability (α=0.833–0.939). Qualitative findings revealed concerns about educator/workforce and resource gaps, and organizational suggestions emphasizing planning and mentorship.
Volume: 15
Issue: 5
Page: 4122-4135
Publish at: 2026-10-01

Teacher readiness and blended learning implementation in Chinese vocational colleges: a quantitative survey study

10.11591/ijere.v15i5.38761
Ding Ge , Mohamad Zuber Abd Majid
Teacher readiness remains a key constraint on blended learning adoption in vocational education, yet limited research has examined how multiple readiness dimensions jointly shape implementation among vocational economics educators in China. This study investigated how technological literacy (TL), attitudes toward technology (AT), educational service quality (ES), and knowledge to create content (KC) predict teacher readiness for blended learning (RL), and how readiness influences actual practice among 390 economics teachers in vocational colleges in Hunan Province, China. A quantitative cross-sectional survey design was employed. Path analysis showed that all four antecedent variables significantly predicted readiness (R²=37.0%), while readiness significantly predicted blended learning practice (β=0.322, p
Volume: 15
Issue: 5
Page: 4080-4092
Publish at: 2026-10-01

Need-supportive instructional leadership, basic psychological needs and teacher professional development in higher education: a multilevel study from Kazakhstan

10.11591/ijere.v15i5.39804
Anara Zhakisheva , Murshida Nurgaliyeva , Bakhyt Galiyeva , Maiya Kayumova , Samal Antikeyeva
Quality of teaching in higher education depends on faculty development and well-being. Drawing on self-determination theory (SDT), this study examined how academic managers’ instructional leadership, operationalized as a management-cycle quality (MCQ) index, relates to faculty outcomes via basic psychological need satisfaction, with organizational culture as context. A cross-sectional study was conducted at Toraighyrov University with 100 staff across 18 departments (50% female; M_age=39.8). Measures included MCQ coding, basic psychological need satisfaction and frustration scale (BPNSFS), Utrecht Work Engagement Scale (UWES-9), Copenhagen Burnout Inventory (CBI), Teachers’ Sense of Efficacy Scale (TSES-12), and trait emotional intelligence (EI). Analyses involved reliability (ω≈.78–.92), confirmatory factor analysis (CFA), inter-rater intraclass correlation coefficient (ICC) (.76–.84), multilevel regression, and bootstrap mediation. Need satisfaction predicted higher engagement (β=.44) and lower burnout (β=−.22), while need frustration predicted higher burnout (β=.39). MCQ independently related to engagement (β=.21) and burnout (β=−.18). Teacher self-efficacy was associated with need satisfaction (β=.31) and trait EI (β=.24). Models explained substantial variance (R² up to .41). Mediation analyses showed that need satisfaction partially mediated MCQ effects on engagement, burnout, and self-efficacy. Findings indicate that need-supportive managerial behavior is a practical lever for improving motivation and well-being, with organizational culture shaping implementation. Limitations include cross-sectional design and modest sample size.
Volume: 15
Issue: 5
Page: 4400-4411
Publish at: 2026-10-01

Constrained finite-control-set predictive control of an H-bridge direct current motor drive

10.12928/telkomnika.v24i5.27909
Bouiri; University of Tindouf Abdesselam , Kendzi; University Center Salhi Ahmed Naama Mohammed , Boughazi; University of Bechar Othmane , Drauoi; University of Tindouf Abdelghani
This paper presents a constrained finite-control-set model predictive control (FCS-MPC) scheme for an H-bridge converter-fed direct current (DC) motor drive, aimed at fast dynamic response and low steady-state armature-current ripple. By selecting the converter switching state directly at each sampling instant, the method removes the separate pulse-width modulation (PWM) stage used in conventional cascaded proportional-integral (PI) control and embeds the armature-current limit directly in the cost function. When the predicted armature current exceeds the predefined limit, a penalty term drives the optimizer to choose the sw itching state that returns the current within safe bounds while preserving speed tracking. The controller is evaluated in simulation under fixed-speed reference tracking, variable-speed reference tracking, and a step load-torque disturbance. The results show fast settling, low steady-state speed error, and bounded current during transients; under the idealized simulation conditions the steady-state armature-current total harmonic distortion (THD) is 0.21 %, indicating low current ripple for the studied operating points. The present study is limited to simulation; experimental validation and a quantitative comparison against conventional PI-PWM control are identified as future work.
Volume: 24
Issue: 5
Page: 1816-1826
Publish at: 2026-10-01

Forecasting particulate matter concentration using deep learning and weather data

10.12928/telkomnika.v24i5.27967
Ahmad; Universitas Indonesia Furqon , Santoso; Universitas Indonesia Soekirno , Naufal; Region II of Meteorological Climatological and Geophysical Agency Ananda , Haryas; Agency for Meteorology Climatology and Geophysics Subyantara Wicaksana , Ahmad; State College of Meteorology Climatology and Geophysics Meijlan Yasir , Tonny; State College of Meteorology Climatology and Geophysics Wahyu Aji , Aviv; State College of Meteorology Climatology and Geophysics Maghridlo , Fadhli; State College of Meteorology Climatology and Geophysics Luthfiansyah , Marzuki; State School of Meteorology Climatology and Geophysics Sinambela , Suko; State School of Meteorology Climatology and Geophysics Prayitno Adi
Particulate matter (PM2.5) concentration is a critical air quality indicator with severe health impacts. Predicting its dynamics is challenging due to complex interactions with meteorological variables. This study evaluates long short-term memory (LSTM) and multilayer perceptron (MLP) networks to identify the most accurate forecasting architecture. We integrated continuous data from automatic weather stations (AWS) and air quality sensors. Data preprocessing involved temporal synchronization, outlier removal, and multivariate imputation by chained equations (MICE). Key input features past particulate matter, temperature, and relative humidity were selected via Pearson correlation and trained using a 24-hour sliding window. Results demonstrate that the single LSTM model outperforms both the MLP and hybrid architectures. The LSTM achieved the lowest root mean square error (RMSE) of 6.862 µg/m³ and a mean absolute percentage error (MAPE) of 37.38%. Although the MLP yielded a slightly higher coefficient of determination (0.810), it exhibited significant magnitude bias by treating sequential data as static features. Ultimately, this research provides a robust, data-driven framework for real-time air quality early warning systems in tropical regions.
Volume: 24
Issue: 5
Page: 1644-1655
Publish at: 2026-10-01

Development model of remote sensing data management for long-term storage using big data

10.12928/telkomnika.v24i5.27639
Riyan; National Research and Innovation Agency Mahendra Saputra , Agnes; National Research and Innovation Agency Sondita Payani , Ogi; National Research and Innovation Agency Gumelar
The growing volume of remote sensing data requires efficient approaches for long-term storage and data management. The National Remote Sensing Data Bank (BDPJN) stores multi-resolution satellite imagery acquired since 1993 to support disaster monitoring, climate studies, natural resource management, and other Earth observation applications. However, managing continuously growing archives remains challenging due to manual migration processes, inefficient storage utilization, and limited scalability. This study proposes an integrated remote sensing data management model that combines hierarchical storage management (HSM) with Apache Spark based parallel processing to support policy-driven data migration and long term storage optimization. The proposed model enables data migration across multiple storage tiers based on data characteristics and storage policies while improving migration efficiency through distributed processing. Experimental evaluation was conducted using Landsat-8, SPOT 6, SPOT-7, and Pleiades datasets under both 1 Gbps and 10 Gbps network environments. The results demonstrate migration performance improvements ranging from 80.14% to 91.04% compared with the existing manual migration system. These findings indicate that integrating HSM with Spark-based parallel processing can reduce migration bottlenecks, improve storage utilization, and provide a scalable and cost-effective approach for managing large-scale remote sensing archives while supporting long-term data governance and accessibility.
Volume: 24
Issue: 5
Page: 1535-1549
Publish at: 2026-10-01

Enhancing SQL injection detection accuracy with fine-tuning LLaMa 3.2 1B using low-rank adaptation

10.12928/telkomnika.v24i5.27884
Diash; Institut Teknologi Nasional Bandung (ITENAS) Firdaus , Idi; Al-Ghifari University Sumardi , Muhammad; Institut Teknologi Nasional Bandung (ITENAS) Ichwan , Maulana Seno; Institut Teknologi Nasional Bandung (ITENAS) Aji Yudhantara
Structured query language injection (SQL injection/SQLi) remains a critical threat to web application security, particularly because it can bypass traditional detection systems through obfuscation. Existing approaches, including rule-based systems and classical machine learning (ML) models, often fail to capture the contextual semantics of com0plex SQL queries. This study proposes a lightweight and context-aware SQLi detection model by fine-tuning the large language model Meta artificial intelligence (LLaMA) 3.2–1B large language model using low-rank adaptation (LoRA). The model was trained using the SQL-Injection-Extend dataset obtained from Kaggle, consisting of 57,310 malicious SQLi samples and 52,208 benign user-input samples. The benign data comprise general user-generated text commonly submitted through web application interfaces. Experimental results demonstrate that the proposed model achieves an accuracy of 99.91%, precision of 99.96%, recall of 99.86%, and an F1-score of 99.91%, outperforming baseline models including convolutional neural network (CNN), long short-term memory (LSTM), and non-fine-tuned large language models (LLMs). These findings indicate that LoRA-based fine-tuning substantially enhances detection performance while maintaining computational efficiency, making the approach suitable for real-time deployment in modern web security systems. Furthermore, the integration of context-aware digital forensic reporting supports post-incident investigation and improves the interpretability of detection outcomes.
Volume: 24
Issue: 5
Page: 1597-1610
Publish at: 2026-10-01

Optimization of MSMEs promotion strategy using GIS with random forest algorithm and A-star algorithm

10.12928/telkomnika.v24i5.26692
Sulyono; Darmajaya Institute of Informatics and Business Sulyono , Suci; Darmajaya Institute of Informatics and Business Mutiara , Agus; Darmajaya Institute of Informatics and Business Rahardi , Sri; Darmajaya Institute of Informatics and Business Lestari , Yulmaini; Darmajaya Institute of Informatics and Business Yulmaini , Ruki Rizal; Darmajaya Institute of Informatics and Business Nul Fikri , Ali; Darmajaya Institute of Informatics and Business Syarifuddin
Micro, small, and medium enterprises (MSMEs) make a main contribution to improving a country’s economy, but it still faces various problems including limited market access, ineffective promotion, and lack of understanding in business profiles. The purpose of this study is to design a geographic information system (GIS) combined with the random forest (RF) algorithm and the A-star algorithm. The RF algorithm functions are to classify MSMEs. Meanwhile, the A-star algorithm is to recommend the shortest route to support accessibility. In addition, it is equipped with a dashboard and MSMEs profile as a reference for stakeholders in making MSMEs development policies and as information to the public in order to expand the reach of promotion in introducing MSMEs and attracting investors, thus optimizing MSMEs promotion strategies. In addition, the evaluation results of the classification model show an accuracy value of 99.6%, indicating that the model is able to predict very well, making it highly recommended for MSME classification.
Volume: 24
Issue: 5
Page: 1526-1534
Publish at: 2026-10-01

Enhancing educators’ digital literacy through digital leadership and organizational culture

10.11591/ijere.v15i5.39153
Sugiarto Sutomo , Heni Rochimah
Despite rapid advances in digital technology and online learning, educators at the education and training center face limited digital access, low information and communication technology (ICT) proficiency, and difficulty integrating technology, reflecting the slow national rise in digital literacy. This study tests whether strengthening digital leadership and aligning organizational culture can improve educators’ digital competencies. A cross-sectional survey of 120 educators was analyzed using path analysis in JASP. Findings indicate that digital leadership strongly predicts organizational culture (β=0.869, z=28.834, p
Volume: 15
Issue: 5
Page: 3777-3789
Publish at: 2026-10-01
Show 12 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration