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31,456 Article Results

Opening the black box of students’ economic behavior in higher education: do formal and informal education really matter?

10.11591/ijere.v15i5.39135
Mohamad Arief Rafsanjani , Waspodo Tjipto Subroto , Luqman Hakim , Handri Dian Wahyudi , Prattana Srisuk
Poor economic behavior and low levels of economic literacy (EL) among university students remain persistent concerns in developing countries, where young adults must navigate increasingly complex economic decisions. Previous studies often conceptualize education as a single construct or examine formal education and family-based learning separately, leaving the psychological mechanisms linking these educational experiences to economic behavior insufficiently understood. Drawing on social cognitive theory (SCT), this study examines the influence of instructional quality as a form of formal education and parental economic socialization as a form of informal education on students’ economic behavior, while investigating the mediating roles of EL and self-efficacy (SE). Data were collected from 291 undergraduate students from faculties of economics and business in East Java, Indonesia, and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that instructional quality and parental economic socialization significantly influence students’ economic behavior both directly and indirectly through EL and SE. These findings indicate that students’ economic behavior is shaped not only by educational environments but also by cognitive and psychological processes. The study highlights the importance of strengthening collaboration between universities and families to foster EL, SE, and responsible economic decision-making among students.
Volume: 15
Issue: 5
Page: 3820-3831
Publish at: 2026-10-01

Interprofessional attitudes, empathy, communication skills, and patient safety competency in traditional Korean medicine and nursing students

10.11591/ijere.v15i5.39879
Mikyoung Lee , Hunpyeong Park
Patient safety competency is an essential part of health professions education and may be influenced by learners’ interprofessional attitudes, empathy, and communication skills. This study investigated the relationships among these variables and identified factors influencing patient safety competency among traditional Korean medicine and nursing students. A cross-sectional correlational study was conducted among 238 students at a university in South Korea, consisting of 150 nursing students and 88 traditional Korean medicine students. Data were collected using questionnaires assessing interprofessional attitudes, empathy, communication skills, and patient safety competency. Data were analyzed using descriptive statistics, t-tests, analysis of variance (ANOVA), Pearson’s correlation coefficients, and multiple regression analysis. Patient safety competency varied by academic year, patient safety education (PSE) experience, college life satisfaction, and major satisfaction. Students with clinical practicum (CP) experience demonstrated significantly higher interprofessional attitudes and patient safety competency than those without practicum experience. Patient safety competency was positively associated with interprofessional attitudes, empathy, and communication skills. Furthermore, PSE experience, major satisfaction, interprofessional attitudes, empathy, and communication skills were significant predictors of patient safety competency, with interprofessional attitudes identified as the strongest predictor. Patient safety competency among traditional Korean medicine and nursing students was influenced more by modifiable educational and interpersonal factors than by fixed background characteristics. These findings support the development of interprofessional, communication-centered, and empathy-informed patient safety curricula to enhance collaborative readiness and safe practice prior to entry into clinical settings.
Volume: 15
Issue: 5
Page: 4220-4231
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

The impact of vocational training on female labor productivity an evaluation in Vietnam’s construction sector

10.11591/ijere.v15i5.39427
Truc Thai Phuong , Lam Nguyen Son
Vietnam’s construction sector has experienced strong growth through rapid urbanization and infrastructure investment. However, female workers continue to face significant barriers, including high informal employment rates (60–65%), 15–20% lower labor productivity than males, and limited access to vocational training (only 19–25% hold certificates). This study investigates the impact of the share of females aged 15+ with vocational training certificates (STFW15+) on female construction labor productivity (FCLP). Using a short time-series dataset (2010–2024, n=15) from General Statistics Office (GSO) of Vietnam, International Labor Organization (ILO), and World Bank, the study applied ForestDiffusion synthetic data pipeline-time-series to generate 75 high-quality synthetic samples (total 90 observations). An artificial neural network (ANN) model integrated with SHapley Additive exPlanations (SHAP) was employed to analyze feature importance and nonlinear interactions. Results show that STFW15+ is the strongest predictor of FCLP (mean |SHAP|=0.514). It exerts a strong positive effect and acts as a powerful moderator, amplifying benefits from income and economic growth while mitigating the negative impact of informal employment. The findings highlight the critical role of gender-responsive technical and vocational education and training (TVET). This study recommends targeted vocational training programs, gender integration in public investment, and public-private partnerships to advance gender equality and support sustainable development goals (SDGs) 5 and 8.
Volume: 15
Issue: 5
Page: 4207-4219
Publish at: 2026-10-01

Evaluating integrity and governance efficiency in blockchain-supported online assessment

10.11591/ijere.v15i5.39221
Aivar Sakhipov , Laura Smagulova , Aigul Yelepbergenova , Alexey Fedenko
With the increasing deployment of remote proctoring systems, there is a noticeable gap in online testing governance due to the lack of credible evidence, which might have adverse implications in terms of trust between the testing platform and its participants. This study, therefore, examines the potential impact of blockchain-powered evidence management on auditability and audit process efficiency in the context of online examinations. In our study, we used an experimental research method and examined the impact of tamper-proof distributed ledger technology on auditability and audit time using a sample of 160 exam sessions divided equally into two groups: a control group (CG, n=80) and an experimental group (EG, n=80). The findings suggest a positive and statistically significant influence of blockchain adoption on auditability (12.5% higher), reaching 96.3% compared to 83.8% in the CG, as well as a 37% reduction in audit time. Furthermore, the inter-rater agreement level reached 0.846. Notably, based on our surveys, test-takers find the process more credible and legitimate without any increased privacy concerns.
Volume: 15
Issue: 5
Page: 4068-4079
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

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

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

A comparative assessment of machine learning, statistical, and hybrid approaches in amending ECMWF rainfall bias in the Batanghari river basin

10.12928/telkomnika.v24i5.27848
Arif; IPB University Ma'rufi , Tania; IPB University June , I Putu; IPB University Santikayasa , Apip; National Research and Innovation Agency (BRIN) Apip , Siswanto; Agency for Meteorology, Climatology, and Geophysics (BMKG) Siswanto
In tropical basins, rainfall predictions using international numerical weather prediction (NWP) models, like the European Centre for Medium-Range Weather Forecast (ECMWF), exhibit significant persistent biases and insufficient ensemble dispersion. This study evaluates statistical, machine learning (ML)-based, and hybrid bias correction methods to improve daily rainfall estimates for Indonesia’s Batanghari River Basin. Findings indicate that the ECMWF model significantly overestimates wet-day frequency (WDF) (81.5% versus 43.4% observed) and underestimates extreme maximum magnitudes, exhibiting an annual maximum rainfall bias of -15.6 mm/day. Standalone ML-based methods, such as random forest (RF) and extreme gradient boosting (XGBoost), experience significant variance deflation, inadequately addressing these substantial underestimations. In contrast, quantile mapping (QM) significantly reduces the intrinsic drizzle bias and achieves the highest monthly temporal stability among the tested frameworks (Kling-Gupta efficiency (KGE) = 0.341) with low processing requirements. Moreover, hybrid methodologies, including RF-QM and XGBoost-QM, successfully restore the natural ensemble spread and robustly reconstruct the upper tails of extreme rainfall, albeit at the expense of long term volume conservation (resulting in negative monthly KGE). Ultimately, QM is favored as an effective baseline for continuous hydrological modeling, whereas hybrid methodologies are highly suited for evaluating localized flash flood hazards and short-term severe intensities.
Volume: 24
Issue: 5
Page: 1611-1631
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

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

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

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

Comparative deep learning architectures for railway train detection using accelerometer

10.12928/telkomnika.v24i5.27752
Heri; Universitas Muhammadiyah Lamongan Ardiansyah , Denny; Universitas Muhammadiyah Gresik Irawan , Dhio; Universitas Muhammadiyah Lamongan Ramadhon Saripudin , Gilang; Universitas Muhammadiyah Lamongan Septia Firmansyah , Rohmatin; Universitas Muhammadiyah Gresik Agustina
Railway level crossing accidents remain a critical safety concern worldwide, necessitating reliable train detection systems particularly at unguarded crossings. This study presents comparative evaluations from four deep learning (DL) architectures convolutional neural network (2D-CNN), long short-term memory (LSTM), MobileNetV2, and hybrid CNN-LSTM of railway train detection using triaxial accelerometer sensor data. Vibration signals acquired from piezoelectric sensors mounted on railway infrastructure were transformed into spectrogram representations and classified into four proximity categories: idle, far-distance (>400 m), near distance (200-400 m), and train passing (
Volume: 24
Issue: 5
Page: 1755-1767
Publish at: 2026-10-01
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