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31,456 Article Results

Facial emotion recognition through artificial ‎intelligence

10.11591/ijai.v15.i5.pp4700-4710
Anas Mohammad Quteishat , Mohammad Hassan , Ashraf Al Sharah , Ahmed Qtaishat
This paper presents a fuzzy min-max deep neural network (FMMDNN) for facial emotion recognition (FER). ‎The fuzzy min-max neural network (FMMNN) will replace the classification layer in the convolutional neural network (CNN). The‎ fuzzy min-max (FMM) exhibits appealing features such as online adaptation, fast training, interpretability, and noise robustness. ‎The fully connected softmax layer in the CNN is replaced by the FMMNN, where hyperboxes will ‎be created and used for classification purposes. The FER2013 dataset is used ‎to test the performance of the proposed method. The empirical results show that adding an FMMNN ‎layer to the CNN network improves the performance.
Volume: 15
Issue: 5
Page: 4700-4710
Publish at: 2026-10-01

Guided through the storm: novice teachers’ experiences of mentoring

10.11591/ijere.v15i5.39817
Beranda Yii Ping Jin , Mohd Muslim Md Zalli , Hasrul Hosshan , Azlina Abu Bakar , Wei Boon Quah
Mentoring is an important part of early career teaching, as novice teachers often face workload pressure, emotional demands, and difficulties in adjusting to school life. However, limited research has examined how mentoring is actually experienced by novice teachers in daily school contexts, particularly within Program Pembangunan Guru Baharu (PPGB) 2.0 in Malaysia. This qualitative study aimed to explore how novice teachers experience PPGB 2.0 in schools in Miri District, Sarawak. Semi-structured interviews were conducted with 12 novice teachers, and the data were analyzed using thematic analysis. The findings identified two main themes: external support and intrinsic support. External support consisted of affective mentor support and school community support. While intrinsic support consisted of: i) self-restorative practices; ii) regulated emotional release; iii) spirituality; and iv) perseverance. The findings show that novice teachers experienced PPGB 2.0 not only as a formal induction structure, but also as relational and personal support that contributed to professional adjustment during the early stage of teaching. The study is context-specific and offers a more grounded understanding of how mentoring is experienced in everyday school life, with useful implications for strengthening induction practices, particularly in mentor preparation and supportive school environments.
Volume: 15
Issue: 5
Page: 3832-3844
Publish at: 2026-10-01

Demographic determinants and interaction effects on parental involvement: a case study from Kosovo

10.11591/ijere.v15i5.39611
Serdan Kervan , Soner Yıldırım , Erdoğan Tezci , Taleh Khalilov
This study examined parental involvement in education among 1,228 parents in Kosovo whose children attend preschool and primary school, with respect to native language, gender, education level, number of children, and the child’s grade level. Findings indicated that the highest parental involvement was in home organization and academic monitoring (HOAM) and the lowest in school-based active participation (SBAP). Multivariate analysis of variance (MANOVA) by native language revealed significant differences across all four dimensions. Discriminant analysis (DA) results showed that SBAP was the strongest discriminator; Albanian-speaking parents scored higher in SBAP and teacher–parent communication (TPC), whereas Turkish-speaking parents scored higher in home-based learning support (HBLS) and HOAM. Interaction analyses showed that language was a stronger discriminator particularly for fathers. The interactions of native language×education level, native language×number of children, and education level×gender were not significant. Overall, the findings demonstrate that the multidimensional structure of parental involvement varies systematically across demographic factors.
Volume: 15
Issue: 5
Page: 4435-4455
Publish at: 2026-10-01

Virtual laboratories and online learning in engineering education: a review

10.11591/ijere.v15i5.39538
Hamza Abu Owida , Areen Arabiat
The rapid expansion of virtual laboratories and online learning in engineering education, accelerated by the COVID-19 pandemic, has created an urgent need for clearer pedagogical guidance on how digital laboratory modalities should be used. This review addresses the unresolved educational problem of ensuring that virtual and remote laboratory environments support not only conceptual understanding but also practical laboratory skills and broader professional competencies. Using a structured review of recent empirical studies, systematic reviews, and meta-analyses, the paper synthesizes evidence on simulation-based laboratories, remote laboratories, immersive environments, and the wider online learning conditions that shape their effectiveness. The review finds that virtual laboratories are consistently effective for conceptual learning, visualization, flexibility, and access, while remote and immersive environments can strengthen authenticity and practical skill development when combined with active learning, multimodal assessment, and strong instructional support. However, persistent limitations remain in psychomotor skill development, assessment validity, accessibility, and institutional readiness. The review concludes that no single modality is sufficient on its own; rather, a hybrid model that pedagogically integrates virtual, remote, immersive, and physical laboratory experiences offers the most defensible pathway for meaningful and sustainable engineering laboratory education.
Volume: 15
Issue: 5
Page: 3857-3869
Publish at: 2026-10-01

The relationship between creativity and programming skills among lower secondary school students

10.11591/ijere.v15i5.39518
Rattanawadee Muensripoom , Mana Sopa
This study addresses the limited emphasis on creativity in lower secondary programming classrooms, where instruction often relies on imitation-based coding practices. To address this issue, the Synectics instructional model was applied to enhance students’ creativity and programming skills. A quasi-experimental design was conducted with 60 students divided into experimental and control groups. Data were collected using creativity and programming skill assessments and analyzed using independent samples t-test and Pearson correlation. The results showed that the experimental group achieved significantly higher scores in creativity (M=9.91, SD=0.95) and programming skills (M=34.67, SD=1.83) than the control group (p
Volume: 15
Issue: 5
Page: 4114-4121
Publish at: 2026-10-01

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

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

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

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

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

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

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

Edge-aware adaptive partial differential equation image inpainting for high-quality restoration

10.12928/telkomnika.v24i5.27914
Fouzia; University of Mohamed El Bachir El Ibrahimi Hamadache , Zine-Eddine; Mohamed Khider University of Biskra Baarir , Nacira; University of Mohamed El Bachir El Ibrahimi Diffellah , Abdelmalik; IEMN UMR CNRS 8520 Ahmed-Taleb , Tewfik; University of Ferhat Abbas Bekkouche , Rabah; University of Mohamed El Bachir El Ibrahimi Hamdini
Image inpainting aims to reconstruct missing or damaged regions in digital images while preserving structural consistency and visual coherence. Among partial differential equation (PDE)-based approaches, the model introduced by Marcelo Bertalmio is a foundational technique for isophote-driven structure propagation. However, its performance often degrades in large damaged re gions. This limitation is mainly caused by insufficient transport strength and isotropic diffusion, which lead to structural blurring. In this paper, we propose an edge-aware adaptive PDE inpainting model that introduces a transport am plification factor and a spatially-adaptive diffusion term to improve edge prop agation and structural preservation. Experimental results on various test im ages demonstrate that the proposed approach improves structural reconstruction. Quantitative evaluation using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics confirms higher restoration accuracy and better edge preservation compared with baseline methods.
Volume: 24
Issue: 5
Page: 1710-1720
Publish at: 2026-10-01
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