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30,938 Article Results

Health locus of control and health behaviour in Minangkabau metabolic disorder patients

10.11591/ijphs.v15i3.27094
Rida Yanna Primanita , Alfian Yanda Putra , Puput Nofia Rahma
Metabolic disorders require sustained health behaviors, yet the psychological predictors of such behaviors may operate differently across cultures. This cross-sectional study examined whether health locus of control (HLOC) predicts multidimensional health behavior among Minangkabau adults with self-reported metabolic disorders in West Sumatra, Riau, and Jambi. A total of 274 participants completed multidimensional HLOC and health behavior scales. Reliability testing, descriptive statistics, Pearson correlations, multiple linear regression, and one-way ANOVA based on dominant HLOC type were conducted. Internal and powerful-others HLOC positively predicted total health behavior, whereas chance HLOC was not significant; however, the overall explanatory power was small (R² = 0.048). Health behavior did not differ meaningfully across dominant HLOC types, with very small effect sizes across diet, sleep, smoking, risky beverage consumption, and exercise (η² < 0.02). These findings suggest that in a collectivistic Minangkabau context, health behavior may be shaped not only by personal control beliefs but also by family, authority, and community norms. The cross-sectional design and self-reported diagnoses limit causal and clinical inference. Family-based education, community leader involvement, and culturally adapted health campaigns are recommended.
Volume: 15
Issue: 3
Page: 678-686
Publish at: 2026-09-01

Predicting student academic success using entry test, language, and spiritual formation data with ensemble learning

10.11591/ijict.v15i3.pp1322-1330
Evander Banjarnahor , Budi Wibawanta , Ronald Belferik , Rijanto Purbojo
Student academic success is influenced by various factors, both academic and non-academic. This study aims to examine the correlation between key student attributes and final grade point average (GPA), as well as to develop a machine learning model to predict academic success. The correlation analysis involved academic variables such as admission scores (Mathematics, English, Indonesian, and academic aptitude test/TPA), English ability test (EAT), spiritual formation (SF), and first-year GPA (GPA_1). The results indicate that GPA_1 has the highest correlation with final GPA (0.63), followed by SF (0.44), while other variables exhibit lower correlations. To enhance prediction accuracy, a machine learning approach using three primary models was employed: Naïve Bayes, support vector machine (SVM), and an ensemble learning method based on a stacking classifier that combines SVM and Naïve Bayes. The evaluation used five train-test split ratios and performance metrics, including accuracy, precision, recall, and F1-score. Experimental results reveal that the SVM model achieves the highest accuracy at 88.40%, followed by the ensemble model combining SVM and Naïve Bayes (88.00%) and the Naïve Bayes model (87.10%). These findings confirm that the machine learning approaches, could effectively predict student academic success, providing a foundation for academic decision-making and educational intervention strategies.
Volume: 15
Issue: 3
Page: 1322-1330
Publish at: 2026-09-01

Enhanced thermal management in 3D integrated circuits coupling

10.11591/ijict.v15i3.pp1208-1216
Vempalle Rafi , Shaik Hussain Vali , Pradyumna Kumar Dhal , Sadhu Radha Krishna , Murkur Rajesh , Malagonda Siva Kumar
3D IC integration, which comprises vertically stacking several IC layers, is one of the new technologies that works well with complementary metal-oxide-semiconductor (CMOS) implementations. The layers of a three-dimensional integrated circuit (3D IC) are physically and electrically connected via copper-silicon bonding and through silicon vias (TSVs). Limitations in 3D IC designs, such as layer-to-layer thermal difficulties and TSV-to-substrate and TSV-to-TSV noise coupling, significantly impact system performance as a whole. Integrating 3D ICs relies heavily on heat spreaders and thermal through silicon vias (TTSVs). Overheating is a common cause of IC failure; however, heat spreaders and FIN to TTSV have been suggested as potential remedies for this problem in the last few years. A 3D IC might melt under the stress of an applied voltage because it becomes hotter inside. Engineers have added fins to the TTSV in a number of ways, each of which maximizes heat dissipation in a different way, in order to reduce this danger. The exceptional thermal cooling characteristics of graphene and carbon nanotubes (CNTs) have led to their widespread dissemination. This research shows that a FIN may efficiently transport thermal energy to a heat sink by using heat spreaders and optimum orientations to distribute heat in all directions. Additionally, we demonstrated the many scenarios in which the IC's potential distribution is impacted by various thermal cooling effects. We found that when it comes to transferring heat away from heat sources and TSVs, CNTs outperform Graphene. We included Al2o3, Si3N4, and SiO2 as examples to examine the consequences of modifying the model's dielectric characteristics.
Volume: 15
Issue: 3
Page: 1208-1216
Publish at: 2026-09-01

Neural network-based diagnosis of type 2 diabetes using an iridology approach

10.11591/ijict.v15i3.pp1226-1237
Alaa Abdulkareem Ahmed , Mohammad Tariq Yaseen
The growing global occurrence of type 2 diabetes requires the development of non-invasive and effective diagnostic methods. This work proposes a novel approach to detecting type 2 diabetes using iridology and machine learning (ML) techniques. By analyzing the iris of the right eye, a single region of interest (ROI) corresponding to the head of the pancreas is recognized for feature extraction. A total of 112 statistical and texture features are extracted using gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. Five neural network (NN) models, narrow, medium, wide, bi-layered, and tri-layered are deployed to classify healthy and diabetic people. The models are trained and assessed using a range of k-fold values (2 to 20) to optimize performance. The highest classification accuracy of 83.2% was reached using the narrow neural network (NNN) model at 7-fold cross-validation. This work exhibts the potential of iridology-based ML approaches for non-invasive diabetes diagnosis, providing a promising substitute to traditional blood tests.
Volume: 15
Issue: 3
Page: 1226-1237
Publish at: 2026-09-01

Smart drones for human detection in disaster response

10.11591/ijict.v15i3.pp1179-1187
Menakadevi Nanjundan , Y. L. Ajay Kumar , V. R. Seshagiri Rao , Nagarjuna Telagam , Seetha Chaithanya , Manikadan S.
Natural disasters require swift, coordinated responses to minimise human casualties and infrastructure damage. This paper presents a novel AI-assisted drone system designed to enhance disaster relief efforts through advanced human detection and a distributed emergency Wi-Fi network. This drone system, equipped with state-of-the-art machine learning algorithms and thermal imaging, excels at locating and identifying individuals even in challenging conditions, such as smoke, debris, or low visibility. The drone fleet operates autonomously, dynamically forming an ad-hoc network that adapts to the evolving needs of the disaster zone. By integrating real-time data processing with efficient network management, our system provides a critical lifeline for communication and a powerful tool for rescuers to navigate and respond effectively. The detection and communication times are observed to be within the range of 5 to 8 seconds for this proposed system, which is widely used in disaster response.
Volume: 15
Issue: 3
Page: 1179-1187
Publish at: 2026-09-01

Early detection of vascular streak dieback (VSD) disease in cocoa plants using deep learning

10.11591/ijict.v15i3.pp1087-1096
Dewi Marini Umi Atmaja , Arif Rahman Hakim , Fadhil Rozi Hendrawan , Komang Diah Devi Pramesty , Naufal Fadhilah Fitrah , Faiz Rochmatullah Widhaputra
Vascular streak dieback (VSD), caused by Ceratobasidium theobromae, poses a significant threat to cocoa (Theobroma cacao) production, leading to substantial yield losses and plant mortality. Early detection is critical to mitigate disease spread and reduce economic impact. While convolutional neural network (CNN) architectures like VGG-16 and ResNet-50 excel in leaf disease detection, no prior studies address stem-based VSD symptomatology where the disease originates in vascular tissues. This study presents the first CNN specifically developed for cocoa stem VSD detection, achieving 99.16% test accuracy with a lightweight architecture that outperforms VGG16/ResNet50 in both accuracy and mobile inference speed. A novel dataset of 215 cocoa stem images was curated and augmented for robustness. Additional evaluation metrics, including precision, recall, specificity, and F1-score, further confirm the reliability of the model. The model was successfully converted into TensorFlow Lite (TFLite) format, enabling deployment on mobile devices for real-time disease detection. This study highlights the potential of integrating deep learning into mobile and drone-based agricultural systems to support precision farming and early intervention strategies.
Volume: 15
Issue: 3
Page: 1087-1096
Publish at: 2026-09-01

AI-powered cardiovascular risk prediction using deep learning images in IoT-blockchain systems

10.11591/ijict.v15i3.pp1016-1025
Deepika Prabhakar , Agusthiyar Ramu
The increasing prevalence of cardiovascular diseases (CVDs) necessitates the development of intelligent, secure, and scalable diagnostic systems capable of accurate and early disease prediction. The importance of this research is to develop a robust and secure system for predicting CVD risk from echocardiogram imaging integrated within an internet of things (IoT) framework and enhanced by blockchain technology. Due to non-invasive feature identification problems and dimensionality, prediction accuracy is degraded due to higher false positives, which leads to lower precision and recall rates. To resolve this problem, implement AI-powered CVD risk prediction based on smart-featured deep learning in Echocardiogram images for an IoT-blockchain environment. The first phase contains data analysis echocardio-dataset vision transformation technique is functional to find the risk level of the disease. The adaptive gaussian filter is applied for normalization process and design a spread-spectral canny edge morphological segmentation and SURF-scaled invariant feature selection for dimensionality scaling. Then, visual geometry network (VGNet) convolutional neural network (CNN) is applied for disease classification. In second phase, the advanced blockchain technology will provide a decentralized and immutable record of patient data, thereby ensuring data integrity and security. The proposed system produces higher performance by analysing the sensitivity specificity as well by ensuring the disease detection level. The blockchain provides higher security to safe in repository for image data for carrying sensitive data with a platform for sharing and validating predictive insights among healthcare providers, researchers, and patients, thus fostering collaborative healthcare efforts.
Volume: 15
Issue: 3
Page: 1016-1025
Publish at: 2026-09-01

A multi-cancer detection framework using deep learning and hybrid machine learning approaches

10.11591/ijict.v15i3.pp1443-1452
Karan Singh , Amruta Pawar , Drishya Tomar , Amrita Yadav , Aditi Chhabria , Vaibhav Narawade
The diagnostic solutions offered by the present artificial intelligence (AI) solutions suffer from non-generalizability and heavy reliance on complex models. In an attempt to solve these issues, we propose a lightweight yet versatile method consisting of a combination of ResNet50 transfer learning and hybrid machine learning. Image features are extracted using dermoscopy, magnetic resonance imaging (MRI), and histopathological images. These are subjected to principal component analysis (PCA) dimensionality reduction followed by classification using support vector machine (SVM), random forest (RF), logistic regression (LR), and XGBoost algorithms. This segregation of the two processes improves efficiency. The hybrid approach using ResNet50 + LR yielded an accuracy of 91.01% in the case of breast cancer detection compared to 86.26% of a baseline convolutional neural network (CNN). Also, ResNet50 gave an accuracy of 96.61% in diagnosing skin cancer. Custom CNN provided an accuracy of 99.42% for lung cancer and 96.33% for brain tumor detection.
Volume: 15
Issue: 3
Page: 1443-1452
Publish at: 2026-09-01

Effectiveness of virtual postpartum education classes on maternal knowledge and health-seeking behavior in Kupang City: a pre-post study

10.11591/ijphs.v15i3.26921
Ignasensia Dua Mirong , Dewa Ayu Putu Mariana Kencanawati , Odi L. Namangdjabar , Hasri Yulianti , Martina Fenansia Diaz , Diyan Maria Kristin
Postpartum maternal health services are routinely administered to postpartum mothers four times daily. In reaction to the COVID-19 pandemic, modifications have transpired in healthcare services, encompassing postpartum maternal health. Virtual homecare services facilitate remote interaction with patients without utilizing healthcare services. This study aims to assess the impact of virtual postpartum maternal classes on knowledge, early detection skills, and treatment-seeking behaviors. This study employs a quantitative methodology utilizing a quasi-experimental pretest-posttest design. A virtual postpartum mothers’ class program was conducted every two weeks for three months. The total population consists of 109 individuals, while the purposive sample comprises 32 individuals. Data acquisition through questionnaires (pre-tests and post-tests). Conduct an analysis of the data utilizing the Wilcoxon signed ranks test. The study's findings demonstrate a substantial impact of virtual postpartum mother class mentoring on knowledge levels (p = 0.000), early detection of complications by postpartum mothers (p = 0.001), and treatment-seeking behavior (p = 0.001). The introduction of virtual postpartum maternal classes significantly enhances postpartum mothers' knowledge, their capacity to identify postpartum complications, and their propensity to seek treatment.
Volume: 15
Issue: 3
Page: 737-744
Publish at: 2026-09-01

Red spinach flour substitution in cookies: effects on iron content, antioxidant activity, and sensory acceptability for anemia prevention

10.11591/ijphs.v15i3.27105
Yeni Tutu Rohimah , Dwi Retna Prihati , Titik Lestari
Iron deficiency anemia affects approximately 30% of Indonesian women of reproductive age, underscoring the urgent need for affordable, iron-rich food interventions. This study investigated the effect of red Amaranth (Amaranthus tricolor L.) flour substitution on iron content, antioxidant activity, and sensory acceptability of composite flour cookies. A completely randomized design was employed with five substitution levels (0%, 10%, 20%, 30%, and 40%), each with three replications, yielding 15 experimental units of cookie batches. Iron content was determined using atomic absorption spectrophotometry, antioxidant activity via the DPPH method, and sensory acceptability through hedonic testing by 30 semi-trained panelists. Results demonstrated that iron content increased significantly (p < 0.05) with higher substitution levels, while antioxidant activity improved from very weak to moderate categories. However, sensory acceptability declined significantly at substitution levels of 30% and above, particularly in color and taste attributes. The 20% substitution level emerged as the optimal formulation, achieving substantially elevated iron content contributing meaningfully toward the recommended dietary allowance per serving, enhanced antioxidant capacity, and maintained good sensory acceptability across all attributes. These findings suggest that red Amaranth-substituted cookies represent a feasible, community-level dietary strategy for complementing existing iron deficiency anemia prevention programs in Indonesia.
Volume: 15
Issue: 3
Page: 839-847
Publish at: 2026-09-01

Performance evaluation of a solar-driven IoT water quality monitoring system using descriptive and ANOVA analysis

10.11591/ijict.v15i3.pp1385-1394
Suziana Ahmad , Arfah Ahmad , Muhammad Uwais Mohammad Raffee , Amirul Syafiq Sadun , Aminurrashid Noordin , Mohd Firdaus Mohd Ab Halim
Water quality monitoring is vital for protecting aquatic ecosystems and ensuring sustainable water resource management. Traditional manual sampling methods are often costly, time-consuming, and unsuitable for real-time assessment. This study presents a newly designed solar-powered IoT-based water quality monitoring system for remote and continuous data collection. The system utilizes an ESP32 microcontroller integrated with pH, temperature, and total dissolved solids (TDS) sensors, powered by a 10W solar panel. Data is transmitted to a cloud-based platform wirelessly, enabling remote access and visualization via a mobile app. Performance evaluation included descriptive statistics and one-way ANOVA across four sampling sites. ANOVA results showed statistically significant differences (p < 0.05) in water quality parameters among locations, confirming the system’s sensitivity. Sensor accuracy was validated against standard meters, revealing mean relative errors below 5% for pH and TDS. The system reliably provides real-time, accurate data, supporting proactive water quality management. Integrating IoT with renewable energy offers a cost-effective, scalable, and energy-efficient solution for environmental monitoring in remote or resource-limited areas.
Volume: 15
Issue: 3
Page: 1385-1394
Publish at: 2026-09-01

Intelligent engineering framework for managing hospital cardiac arrest resources

10.11591/ijict.v15i3.pp1290-1302
Chams Eddine Fathoun , Mohamed Ridda Laouar , Safa Abid , Sean B. Eom
In-hospital cardiac arrest in intensive care remains frequent (often cited incidence roughly 0.5%-7.8% of admissions), while causes differ in what staff and equipment must be ready. We ask whether vital-sign trajectories from a standard EHR can classify which of three cardiac-related mechanisms is most salient arrhythmia, acute myocardial infarction (AMI), or respiratory failure or hypoxia so ICU resources can be aligned with risk. Using MIMIC-IV, we extracted diagnoses and charted vitals in the 12 hours before the index event, applied cleaning, aggregation, label encoding, sequence padding, and class balancing (3,000 cases per class), then trained and compared eXtreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and logistic regression (LR) with 5-fold cross-validation on an 80/20 split. XGBoost performed best (about 93% accuracy; sensitivity 89.15%; specificity 90.43%; AUC-ROC 0.94). Feature importance highlighted heart rate, oxygen saturation, and blood pressure patterns consistent with bedside monitoring practice. The study supports mechanism-oriented triage labels derived from widely recorded vitals, as a complement to generic early warning scores, for prioritizing telemetry, respiratory support, and cardiology pathways. External validation and prospective evaluation are needed before deployment.
Volume: 15
Issue: 3
Page: 1290-1302
Publish at: 2026-09-01

Lightweight parallel feedback network based on CRL with policy transfer and enhancement for image super-resolution

10.11591/ijict.v15i3.pp944-954
S V R Manimala , T Kavitha
Image super-resolution (SR) is essential in applications such as surveillance, medical imaging, and remote sensing, but existing deep learning (DL) models often require high computational resources and struggle to recover fine details in lightweight architectures. Although feedback and attention based methods have shown improvements, they still lack an effective combination of efficient feature refinement, edge enhancement, and low parameter complexity. To address this gap, we propose a lightweight parallel feedback network (LPFN) that combines three key components: a feedback block for repeated feature refinement, a dispersion-aware attention residual block (DARB) for highlighting important spatial and channel details, and EdgeNet for edge sharpening for sharper boundaries. These components are supported by curriculum reinforcement learning (CRL), an adaptive training strategy that gradually improves the model’s learning behavior. Instead of relying on a fixed loss function, LPFN uses a dynamically learned global feedback loss to refine reconstruction quality at each stage. Experiments on DIV2K and Flickr2K show that LPFN achieves higher PSNR and SSIMscores while keeping the model lightweight and efficient. This study emphasizes an effective lightweight feedback framework, an enhanced attention and edge-refinement mechanism, and an adaptive learning strategy that improves both accuracy and stability under different degradation conditions.
Volume: 15
Issue: 3
Page: 944-954
Publish at: 2026-09-01

Navigating digital parenting: a bibliometric exploration of trends on children’s digital soothing practices

10.11591/ijict.v15i3.pp1431-1442
Rita Wong Mee Mee , Noor Hanim Harun , Lim Seong Pek , Suzulaikha Mohamed , Tengku Shahrom Tengku Shahdan , Nurul Asyiqin Jalil , Anisa Ahmad , Tirzah Zubeidah Zachariah
The digital age has transformed parenting practices, with an increasing reliance on digital devices for managing children’s behavior, particularly as calming tools. This study addresses the growing phenomenon of digital parenting, highlighting its implications on child development and family dynamics. Despite the benefits of digital media, concerns persist regarding its overuse for emotional regulation, which may impede children’s self-regulation skills and parent-child interactions. This study aims to explore the evolution of research on digital parenting using bibliometric analysis. A comprehensive dataset was extracted from the Scopus database, focusing on publications from 2020 to 2024 within the Social Sciences domain. The inclusion criteria included peer-reviewed, open-access articles written in English. A systematic methodology ensured the analysis of performance metrics, trends, and co-authorship patterns. Results indicate a significant increase in scholarly attention to digital parenting, with 837 articles meeting the inclusion criteria. Leading contributions emerged from journals such as Sustainability Switzerland and Education Sciences, with prolific authors and institutions from the United Kingdom and the United States dominating the field. The analysis underscores the interdisciplinary nature of the topic, reflecting contributions from education, media studies, and child development. This study offers valuable theoretical insights and practical recommendations, emphasizing balanced digital media use and informed parenting strategies to foster healthier family dynamics.
Volume: 15
Issue: 3
Page: 1431-1442
Publish at: 2026-09-01

Advanced materials for crosstalk and power optimization in TSV-enabled 3D ICs

10.11591/ijict.v15i3.pp1143-1153
Tappeta Chinna Sanjeeva Rayudu , Merrin Prasanna Nagadasari
The continued scaling of semiconductor devices has exposed the limitations of traditional two-dimensional (2D) integrated circuit architectures. To address performance bottlenecks and interconnect constraints, the industry is increasingly adopting three-dimensional (3D) integration technologies. through-silicon vias (TSVs) are a fundamental enabler of this advancement, facilitating vertical signal transmission between stacked silicon layers. Despite their benefits, TSVs face critical challenges related to crosstalk, power dissipation, and signal delay issues that are especially pronounced in dense via arrays. This research explores the use of multi-walled carbon nanotube (MWCNT) based TSVs insulated with different dielectric liners, including silicon dioxide (SiO₂), PPC, polyimide, and benzocyclobutene (BCB). HSPICE simulations are used to evaluate crosstalk noise, power dissipation, power delay product (PDP), and energy delay product (EDP) across varying TSV pitches. Among the materials studied, BCB demonstrates the most promising results. Specifically, MWCNT TSVs with BCB at a 10,000 μm pitch achieve up to 58% reduction in functional crosstalk, 75% in dynamic crosstalk, 78% in power dissipation, and a 52% improvement in PDP compared to single-walled CNT (SWCNT) based TSVs. These findings confirm the suitability of combining MWCNT cores with low-k BCB liners for enhancing performance, energy efficiency, and signal reliability in advanced 3D integrated circuits.
Volume: 15
Issue: 3
Page: 1143-1153
Publish at: 2026-09-01
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