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30,907 Article Results

Examining dual-factor mental health: a confirmatory factor analysis among senior secondary students in Islamic boarding schools

10.11591/ijere.v15i4.39833
Rahmat Aziz , Esa Nur Wahyuni , Wildana Wargadinata , Ali Maksum , Retno Mangestuti , Iffat Maimunah
This study examines the measurement properties of the dual-factor model of mental health among senior secondary students in Islamic boarding schools. Despite growing interest in the dual-factor framework, empirical validation in culturally embedded educational settings remains limited. Using a quantitative cross-sectional design, data were collected from 616 students across 10 schools using the Azira Mental Health Scale (AMHS-24), which assesses psychological well-being and psychological distress. Confirmatory factor analysis (CFA) supported a two-factor structure, indicating that well-being and distress are distinct yet related constructs. The model showed acceptable but not optimal fit (root mean square error of approximation (RMSEA)=.055; standardized root mean square residual (SRMR)=.063; comparative fit index (CFI)=.874; Tucker–Lewis index (TLI)=.862). Reliability was satisfactory (α=.855 and .861), and discriminant validity was supported (heterotrait–monotrait (HTMT)=.321). However, convergent validity was limited (average variance extracted (AVE)=.332 and .343). The novelty of this study lies in validating a dual-factor mental health instrument within Islamic boarding schools, providing a contextually grounded assessment of both positive and negative dimensions. Despite this limitation, the findings support the use of the AMHS-24 as a reliable tool for assessing general mental health patterns.
Volume: 15
Issue: 4
Page: 3141-3150
Publish at: 2026-08-01

Development of ergonomic skills of future teachers of preschool organizations based on klax pedagogy

10.11591/ijere.v15i4.39441
Feruza Abdrimova , Sholpan Kolumbayeva , Aliya Kosshygulova , Gulnur Amirzhanova , Saltanat Khassanova
Training future preschool teachers requires the systematic development of ergonomic skills due to the high physical and emotional demands of their professional activity. However, existing teacher education programs often emphasize theoretical knowledge while providing limited opportunities for the development of practical ergonomic competencies. This study addresses this gap by investigating the effectiveness of klax pedagogy, a movement-oriented and experiential approach, in dev eloping ergonomic competence among future preschool teachers. A quasi-experimental pre-test/post-test control group design was employed with 84 students (experimental group: n=42; control group: n=42). The experimental group participated in klax-based activities focused on posture control, movement coordination, spatial organization, and ergonomic awareness, while the control group received traditional instruction. The results revealed a statistically significant improvement in ergonomic skills in the experimental group (t=9.84, p
Volume: 15
Issue: 4
Page: 3489-3496
Publish at: 2026-08-01

ChatGPT as scaffold: quiz performance across session complexity

10.11591/ijere.v15i4.39915
Fatima Ezzahra Kabba , Zouhair Ejbari
Many studies examine the use of ChatGPT in education, but most measure student perceptions, not performance, and few track performance across multiple sessions of different complexity. In particular, no study has tracked whether this association varies across sessions of different cognitive complexity. Using a quasi-experimental design, first-year undergraduates (N=193) at the Higher International Institute of Tourism (ISITT) in Tangier, Morocco were followed across seven introductory statistics sessions. One group had access to ChatGPT during learning activities, while the other followed the same instruction without artificial intelligence (AI) access. Performance was measured through end-of-session quizzes (1,136 observations) and analyzed using a linear mixed-effects model. No consistent overall advantage was associated with either condition. However, a significant interaction between condition and session was identified (χ²(6)=61.50, p
Volume: 15
Issue: 4
Page: 3172-3181
Publish at: 2026-08-01

Formation of professional competencies of future biology teachers based on STEM technologies

10.11591/ijere.v15i4.38133
Symbat Ibadulla , Kalampyr Zhumagulova , Gulmira Zhussipova , Makhabbat Amanbayeva , Gani Issayev , Ardak Bostanova
This study developed an integrated pedagogical model incorporating Python programming, Arduino biosensors, and virtual reality (VR) applications aimed at biology teacher training. The 20-week study involved 124 participants (62 in the experimental group (EG) and 62 in the control group (CG)), with baseline competency levels assessed at the outset. Through collaborative projects, participants created technological applications integrated with biological content. Subject knowledge scores in the EG increased by 42.2% (from 22.3 ± 4.1 to 31.7 ± 3.2), compared to a 14.9% increase in the CG (from 22.1 ± 3.9 to 25.4 ± 3.7) (p 
Volume: 15
Issue: 4
Page: 3542-3556
Publish at: 2026-08-01

A blended learning model for creative thinking development

10.11591/ijere.v15i4.38784
Nuttapong Prasertsung , Alisa Songsriwittaya
Creative thinking is a critical competency in higher education in the industry 5.0 era. Although blended learning (BL) and design thinking (DT) are widely adopted, existing instructional approaches often lack explicit mechanisms for process-level quality regulation across the creative process, leading to inconsistent and insufficiently structured outcomes. To address this gap, this study develops and validates the blended learning design thinking plan-do-check-act (BLDTPDCA) learning model, which embeds the plan-do-check-act (PDCA) cycle as a stage-based quality regulation mechanism within the DT process, rather than as a separate tool. The model incorporates a task–modality alignment linking face-to-face learning with the empathize, define, and ideate stages, and online learning with the prototype and testing stages to support continuous feedback. The study employed a model development and validation design. Five experts evaluated the model using a 19-item index of item-objective congruence (IOC), indicating high content validity (IOC=0.95). The findings demonstrate that the model enables systematic, process-level quality regulation within creative learning activities. This study contributes to the literature by reconceptualizing PDCA as an embedded regulatory mechanism within DT and by providing a structured, theory-informed framework for aligning instructional processes with measurable creative thinking development in higher education.
Volume: 15
Issue: 4
Page: 3228-3240
Publish at: 2026-08-01

Digital divide and fairness perceptions of computer-based testing in Vietnam

10.11591/ijere.v15i4.37322
Tran Thi Hoa Tien , Phan Thi Thuy An , Do Huy Duc , Le Ha Giang , Ngo Thi Minh Hanh , Tran Thi Ngoc Anh
Equity concerns are central to large-scale shifts toward computer-based testing (CBT), yet evidence from developing systems remains limited on whether institutional supports can offset household digital inequalities. This study surveyed 1,197 Vietnamese high school students (grades 10–12) using a 20-item CBT acceptance scale (α=.94) and two single-item fairness indicators. Overall acceptance was moderately positive (M=3.54, SD=0.74), whereas fairness perceptions were markedly weaker: only 31.7%–38.2% agreed that CBT ensures fair scoring and 55.1%–66.7% worried that CBT could create inequality, reflecting unequal fairness perceptions across areas, with concerns strongest in rural settings. Group comparisons showed small but consistent advantages in acceptance for students with prior CBT experience and school-level computer access (g≈.16–.20), while home computer ownership had negligible association (g≈.01). Acceptance did not differ by residential area (η²≈.001), but fairness concerns varied across areas. These findings suggest that institutional exposure and governance practices—rather than household device ownership—are the most actionable levers for equitable CBT implementation. Practical implications include strengthening school-based access and practice opportunities, transparent proctoring and contingency procedures, and integrating fairness monitoring into CBT dashboards.
Volume: 15
Issue: 4
Page: 3075-3085
Publish at: 2026-08-01

Deep learning for categorizing microsatellite stability in colorectal cancer

10.11591/ijai.v15.i4.pp3761-3769
Sofyan El Idrissi , Yassine Drider , Ikram Ben Abdel Ouahab , Mohammed Bouhorma , Fatiha El Ouaai
Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.
Volume: 15
Issue: 4
Page: 3761-3769
Publish at: 2026-08-01

Hybrid quantum-classical neural networks for brain computed tomography scan diagnosis

10.11591/ijai.v15.i4.pp3342-3351
Bilal R. Altamer , Muhamad Azhar Abdilatef Alobaidy , Aws Hazim Saber Anaz , Zahraa Tarik AlAli
Medical image classification is considered as very important field of diagnosis and treatment of neurological disorders, which include stroke, tumors, and hemorrhages, it can used to facilitate timely medical intervention. A hybrid quantum-classical convolutional neural network (QCNN) is presented by combining quantum information processing with classical deep learning techniques for improved feature extraction and classification accuracy. The model combines convolutional neural network (CNN), which handles initial feature extraction with a PennyLane and TensorFlow based layer that employs quantum entanglement and superposition principles to enhance classification performance. The model is trained and tested over a computed tomography (CT) scan image dataset which has four classes (normal, stroke, tumor, and hemorrhage). Various learning rates are tested and employed a hybrid backpropagation method to improve efficiency. Confusion matrices, region of conversion receiver operating characteristic (ROC) curves, and plots for the training convergence that all pointed to promising classification accuracy were derived with a detailed analysis accordingly. Overall, the results indicate that combining quantum computing with deep learning architectures could improve classification performance at a lowest computational cost. The results suggest the viability of hybrid quantum-classical models for medical imaging applications and indicate that quantum computing is a promising direction for enhancing diagnostic accuracy in the area of radiology.
Volume: 15
Issue: 4
Page: 3342-3351
Publish at: 2026-08-01

Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability

10.11591/ijai.v15.i4.pp3103-3119
Nur Diana Izzani Masdzarif , Siti Azirah Asmai , Yogan Jaya Kumar , Muhammad Hafidz Fazli Md Fauadi
Technical energy loss estimation in power distribution systems is essential for improving operational efficiency and cost-effectiveness. However, distribution-level datasets are often failed to cope with the nonlinear behavior and sparse, low-resolution data typical in modern grid environments. This study proposes an interpretable artificial intelligence (AI) framework based on least absolute shrinkage and selection operator (LASSO) regression integrated with Shapley additive explanations (SHAP) to estimate and explain technical energy losses at the feeder level. An exploratory multicollinearity assessment using correlation analysis and variance inflation factor (VIF) revealed severe redundancy among operational variables, justifying the adoption of L1-regularized regression. Hyperparameter tuning via LassoCV identified an optimal regularization parameter, resulting in strong predictive performance. Comparative evaluation with nonlinear models, including random forest and gradient boosting, demonstrated that LASSO achieves competitive or superior generalization performance while preserving interpretability. Feature importance analysis and SHAP-based explanations confirmed that operational loading variables particularly infeed energy, load factor, and maximum demand are the dominant drivers of technical losses. SHAP dependence and interaction analyses further revealed context-dependent behavior among correlated predictors, enriching interpretability beyond coefficient-based rankings. The results demonstrate that regularized linear modeling, when combined with explainable AI techniques, provides a robust, transparent, and practically deployable solution for technical loss estimation in distribution networks.
Volume: 15
Issue: 4
Page: 3103-3119
Publish at: 2026-08-01

Utilizing Poisson modeling and advanced machine learning techniques for enhanced detection of slowloris attacks

10.11591/ijai.v15.i4.pp3154-3163
Prathima Mabel John , Mamatha Ram , Vani Kurugod Ashwathanarayana Reddy , Santhosh Babu , Rama Mohan Babu Kasturi Nagappasetty
This paper presents a secure framework for detecting and mitigating slowloris attacks in software defined networks (SDN) using Poisson arrival modeling to generate attack traffic. Slowloris attacks imitate legitimate traffic patterns, making them difficult to identify. The framework includes three modules: a data generation module, a feature selection module based on the blended statistical and information gain (BSIG) approach, and a traffic classification module using machine learning and deep learning algorithms. Poisson arrival modeling captures the stochastic nature of attack traffic by introducing variability in packet inter-arrival times, thereby simulating realistic network conditions. Monte Carlo simulations establish anomaly thresholds, while the BSIG method identifies key features, such as inter-arrival variability and CPU utilization. Support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) classifiers achieve high precision in distinguishing legitimate traffic from attack traffic. The integration of Poisson modeling with advanced analytical methods improves detection capability while maintaining scalability for similar stealth attacks. The proposed methodology provides high detection accuracy and adapts to dynamic network environments, enhancing web server defenses against slowloris attacks in real-world scenarios. The framework also reduces false positives, supports efficient resource utilization, and strengthens proactive security management in modern SDN infrastructures under varying conditions.
Volume: 15
Issue: 4
Page: 3154-3163
Publish at: 2026-08-01

Context-aware AgriBot using dual intent and entity transformer and hybrid deep learning model

10.11591/ijai.v15.i4.pp3637-3645
Binod Deka , Ridip Dev Choudhury , Utpal Barman
The agriculture sector has gone through vast technological improvement, leading to increased productivity and sustainability. This research introduces AgriBot, a context-aware virtual assistant (VA) that helps farmers in rice cultivation and identifies diseases while providing instant suggestions for subsequent task. Using the RASA framework, AgriBot has been designed to understand the farmer's queries. Dual intent and entity transformer (DIET) classifier has been used for entity classification, achieving training and testing accuracy of up to 98% and 97%, respectively. Additionally, the system incorporates machine learning (ML) models for rice disease detection, utilizing a dataset of 4,624 images covering three major rice diseases: bacterial blight, brown spot, and blast. Among the tested models—neural network (NN), random forest (RF), support vector machine (SVM) and naive Bayes (NB) achieved an accuracy of up to 99.9%, demonstrating excellent classification performance. Using text-based query handling with image-based disease identification and instant suggestion makes it a more robust support system for the farmers.
Volume: 15
Issue: 4
Page: 3637-3645
Publish at: 2026-08-01

Design of intelligent embedded system for personal protective equipment detection and face recognition access control

10.11591/ijai.v15.i4.pp3176-3188
Mariam Mesfer , Aoosh Matar , Reem Saif , Alyazy Saleh , Irfan Ahmed , Moath Awawdeh , Anees Bashir
This paper presents an artificial intelligence (AI)-powered automated access control system that aims to reduce delays and improve safety. The primary problem addressed is effective monitoring of compliance with personal protective equipment (PPE) and secure access control for personnel entering sites. This study represents the design and development of an access control system that includes accurate detection of essential PPE items (e.g., safety helmets, gloves, goggles, and gas detectors), integration of facial recognition for identity verification, real-time monitoring of video feeds, and an intuitive user interface for security personnel to manage access and compliance efficiently. The software part uses you only look once (YOLO) version 8 for real-time object detection, classification, and drawing the bounding boxes around the detected object in a single forward pass. The hardware platform consists of NVIDIA Jetson AGX Orin 64 GB as an edge computing device. The developed AI-based embedded system is tested and validated with real-world scenarios and achieved a mean average precision (mAP) of 98.4% for PPE detection and 99.38% accuracy for face recognition.
Volume: 15
Issue: 4
Page: 3176-3188
Publish at: 2026-08-01

Bridging gaps in health artificial intelligence: challenges in MDPI research articles

10.11591/ijai.v15.i4.pp3053-3067
Irwan Bastian , Aqilla Rahman Musyaffa , Lukman Nulhakim , Novia Putri Bahirah , Dewi Agushinta R.
Technological advancements in artificial intelligence (AI) have transformed healthcare by improving early disease detection, personalized treatment, predictive analytics, and clinical decision support systems. However, AI adoption in healthcare faces critical challenges, including data privacy concerns, algorithmic bias, regulatory barriers, usability issues, and system interoperability. Addressing these issues requires standardized regulations, ethical frameworks, and interdisciplinary collaboration to ensure responsible AI integration. This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer. The review focuses on Multidisciplinary Digital Publishing Institute (MDPI) journal articles to identify key contributors, emerging trends, and research gaps in AI-driven healthcare. Findings highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics, while exposing persistent challenges such as a lack of standardized AI models, ethical concerns, and accessibility disparities. By mapping the research landscape, this study provides evidence-based insights and recommendations to address AI adoption barriers, improve transparency, and guide future research in healthcare AI. The results contribute to developing a more equitable, efficient, and trustworthy AI-driven healthcare system.
Volume: 15
Issue: 4
Page: 3053-3067
Publish at: 2026-08-01

Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling

10.11591/ijai.v15.i4.pp3421-3430
Iwan Ady Prabowo , Hendro Wijayanto , Teguh Susyanto
Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Volume: 15
Issue: 4
Page: 3421-3430
Publish at: 2026-08-01

Determining student scaffolding levels in geometry problem-solving: a fuzzy inference system using Mamdani method

10.11591/ijai.v15.i4.pp3286-3298
Yuniar Ika Putri Pranyata , Susiswo Susiswo , Tjang Daniel Chandra
This study aims to adopt a fuzzy logic inference system using Mamdani method to determine the appropriate scaffolding level for students based on errors in solving geometry problems. The fuzzy inference system (FIS) assessed the students' understanding and provided scaffolding to address specific learning needs by analyzing common mistakes in geometry problem-solving. This method optimized the educational process by offering personalized support, enhancing students' problem-solving skills, and reducing error rates. The data processing criteria using the FIS involved analyzing the scores of mathematics education students at a private university in Malang when solving geometry problems, based on Pólya's stages. Mamdani was used to provide recommendations based on students' cognitive data while solving geometry problems, in line with scaffolding components. The results showed that the system personalized scaffolding levels for students working on geometry problems. This contributed to the field of educational technology by presenting a new approach to adaptive learning and emphasized the importance of personalized pedagogical support. The approach was particularly beneficial for geometry teachers who provided individualized scaffolding based on students' errors, contributing to improved learning outcomes.
Volume: 15
Issue: 4
Page: 3286-3298
Publish at: 2026-08-01
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