Articles

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

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,938 Article Results

Consumer preferences and marketing strategy for black pule (Alstonia spectabilis) antimalarial tablet prototypes in South Central Timor

10.11591/ijphs.v15i2.26853
Novia Maulina , Sari Dewi Setyowati , Salma Rizqika Irwanadi , Hajar Sugihantoro , Riza Ambar Sari , Ziyana Walidah , Maximus M. Taek , Burhan Ma’arif
Malaria remains endemic in eastern Indonesia, and resistance to conventional antimalarial drugs necessitates alternative treatments. Black pule (Alstonia spectabilis) is one of the plants that has been researched and claimed to be a natural ingredient that can treat malaria. Black pule prototype has been prepared for the downstream stage through commercialization. Before that, it is necessary to conduct marketing research to ensure the success of the marketing. Therefore, this study evaluates consumer preferences and formulates marketing strategies for black pule antimalarial tablets. This study involved 100 respondents selected using cluster and purposive sampling across three malaria-endemic sub-districts in South Central Timor Regency. Using a quantitative-descriptive approach, data were collected through questionnaires and analyzed with descriptive statistics. The results show that most consumer targets stated that the antimalarial tablet prototype of black pule has a good impression and characteristics. Approximately 69-77% of respondents rated the product positively across shape, color, taste, aroma, size, and overall organoleptic attributes. The results indicate that the majority of respondents gave positive evaluations and expressed a preference for the product’s shape, color, taste, aroma, size, and overall organoleptic characteristics. Findings support aggressive marketing strategies for herbal antimalarial tablets in endemic regions. Based on these findings, an aggressive S-O marketing strategy is recommended, emphasizing product promotion, participation in health-related expos, strengthened digital outreach, and concise educational initiatives to improve public acceptance of the herbal antimalarial tablet.
Volume: 15
Issue: 2
Page: 346-359
Publish at: 2026-06-01

Effectiveness of Afigel therapy in reducing urinary incontinence among premenopausal women: a quasi-experimental study in rural Indonesia

10.11591/ijphs.v15i2.26945
Siti Fatonah , Sulastri Sulastri
Urinary incontinence (UI) affects millions globally, predominantly women, significantly impacting quality of life. This study evaluated Afigel as a novel intervention for reducing UI symptoms. A quasi-experimental pre-posttest design was employed with 93 premenopausal women randomly assigned to intervention (n = 31) and control (n = 62) groups. Data collection utilized personal data sheets, Afigel therapy standard operating procedures, observation sheets, and the questionnaire for urinary incontinence diagnosis (QUID). Dependent and independent t-tests analyzed the data. Among participants, 43% experienced stress incontinence, 47% experienced urge incontinence, and 10% mixed incontinence. Severity was mild (41%), moderate (45%), and severe (14%). The intervention group demonstrated a significant reduction in incontinence scores from 6.30 to 1.53 (p = 0.000), while the control group showed minimal change from 6.30 to 6.18 (p = 0.21). Afigel demonstrates potential as an effective UI management therapy. These findings suggest Afigel could substantially improve quality of life by reducing UI-related social embarrassment, activity limitations, and psychological distress. For primary health care settings, Afigel offers a potentially accessible, non-invasive treatment option that community health nurses and primary care providers could implement, reducing referral burdens on specialist services while addressing this prevalent yet often underreported condition.
Volume: 15
Issue: 2
Page: 458-465
Publish at: 2026-06-01

Personalized learning with learning style using fuzzy for university students performance

10.11591/ijai.v15.i3.pp2216-2228
Endina Putri Purwandari , Endang Widi Winarni , Siti Soraya Abdul Rahman , Jafar Nashrudin Al Azam
The main challenges of traditional learning systems are time-space constraints and teacher-centeredness. The emergence of information technology has given rise to e-learning systems characterized by teacher centred strategy components and one-size-fits-all strategies. Furthermore, the concept of personalization is presented through learning technology that provides educational content to the students learning style. This research develops a personalized system that aligns teaching strategies with students' learning styles using the Myers-Briggs Type Indicator (MBTI). The emphasis is on adaptive and revising teaching strategies to improve student learning performance. The system is developed to create student profiles to determine their learning styles based on the MBTI and fuzzy. The system was tested with undergraduate students at the information systems department in University of Bengkulu. Research shows that students in the experimental group have higher post-test scores, greater learning achievement and performance than the control group. Fuzzy clustering based personalized e-learning could improve university student performance. The use of personalized online learning significantly affects learning management system (LMS) integration, lecturers, and curriculum development.
Volume: 15
Issue: 3
Page: 2216-2228
Publish at: 2026-06-01

Development of a supportive-educative health education intervention for family caregivers of diabetes mellitus patients: a quasi-experimental study

10.11591/ijphs.v15i2.27034
Siti Fatonah , Sulastri Sulastri , Yuniastini Yuniastini
Despite growing evidence on family involvement in diabetes management, few studies have developed and tested structured educational programs targeting family caregivers as active companions in resource-limited settings. This study aimed to evaluate the effectiveness of a comprehensive family support education program based on the five pillars of diabetes mellitus (DM) management in enhancing family knowledge and educational support capacity. A quasi-experimental design with non-probability sampling was employed, involving 157 respondents (intervention group: 72; control group: 85). Social support was measured using a modified Diabetes Social Support Questionnaire-Family. Data were analyzed using independent and paired t-tests. The intervention group demonstrated a significant increase in mean educational support scores from 61.81 to 90.85 (mean difference = 29.04; p < 0.001; Cohen's d = 1.82), indicating a large effect size. The control group showed negligible changes. Poor knowledge decreased from 45.8% to 26.39% in the intervention group, while intermediate knowledge increased substantially. The structured family education program produced a large and statistically significant improvement in caregiving capacity. These findings underscore the need for public health policies to integrate family-centered educational interventions into routine diabetes care, particularly in settings with limited healthcare infrastructure, to reduce disease burden and improve patient outcomes.
Volume: 15
Issue: 2
Page: 378-388
Publish at: 2026-06-01

Expert-derived indicators for evaluating design thinking prototypes in teacher education

10.11591/ijere.v15i3.38476
Mary Cris J. Go , Jovelyn G. Delosa , Christine C. Royo
Evaluating design thinking prototypes in teacher education remains challenging due to the absence of standardized and theory-aligned evaluation criteria. Existing assessment practices are often inconsistent and insufficiently aligned with the process-oriented nature of design thinking–based research. This study aimed to develop expert-derived indicators to inform the construction of a prototype evaluation instrument for teacher education research. Using a qualitative instrument development approach, a focus group discussion (FGD) was conducted with research advisors and panel members experienced in evaluating undergraduate and graduate research outputs. Data were analyzed using Braun and Clarke’s reflexive thematic analysis, resulting in six evaluation domains: problem alignment and functional relevance, novelty and intellectual property integrity, standardization of evaluation practices, alignment with design thinking processes, feasibility and sustainability, and demonstration of research and professional competencies. These domains were operationalized into a pool of observable indicators representing key dimensions of prototype quality. The resulting domains provide initial content validity evidence for the development of a standardized prototype evaluation instrument, supporting subsequent content validation and psychometric testing. The findings offer an expert-informed evaluation framework that contributes to more rigorous, transparent, and consistent assessment of design thinking prototypes in teacher education and higher education research.
Volume: 15
Issue: 3
Page: 2305-2312
Publish at: 2026-06-01

Vietnamese secondary school EFL students’ motivation and attitudes toward using ChatGPT for vocabulary learning

10.11591/ijere.v15i3.38603
Thuan Anh Pham , Cuong Huy Pham
The escalating adoption of artificial intelligence (AI) in language education, particularly ChatGPT, has drastically altered vocabulary teaching and learning practices in secondary schools in Vietnam, where rote learning and teacher-centered approaches are common norms. This study examines secondary school students’ motivation and attitudes toward using ChatGPT for vocabulary development. A mixed-methods design was employed, drawing on a survey with 208 ninth-grade students and semi-structured interviews with eight of the respondents. The survey data revealed the students’ moderate degrees of motivation to use ChatGPT, with extrinsic motivation surpassing intrinsic motivation. Their attitudes were generally positive, showing relatively high enjoyment and low anxiety in utilizing ChatGPT for vocabulary learning. The students considered ChatGPT a useful and convenient resource and expressed willingness to continue using it despite their concerns over its reliability and the risk of over-reliance. The interview data augmented these patterns, highlighting ChatGPT’s interactivity and flexibility while emphasizing the continued importance of teachers’ classroom support. This study provides corroborating evidence for ChatGPT as a versatile complementary tool for vocabulary learning that fosters learners’ motivation and positive attitudes. However, its adoption requires careful integration with teacher guidance and AI literacy training.
Volume: 15
Issue: 3
Page: 2708-2715
Publish at: 2026-06-01

AMAC-LW: Adaptive medium access control for long range wide area network with energy-aware routing

10.11591/ijece.v16i3.pp1626-1644
Sowmya M. , S. Meenakshi Sundaram , Pandiyanathan Murugesan , Santhosh Kumar K. S. , Tejaswini R. Murgod
To enhance the performance of long range wide area network (LoRaWAN), a routing algorithm and a novel medium access control (MAC) layer protocol are required. In addition to addressing scalability and security issues, the protocol seeks to improve communication efficiency, dependability, and power consumption. It presents a dynamic routing method that reduces energy consumption by utilizing machine learning processes, adaptive routing tactics, and route optimization approaches. Simulations in a range of deployment situations are used to assess the suggested solutions. These results imply that the suggested protocol and routing scheme have the potential to greatly enhance the sustainability, energy efficiency, and performance of LoRaWAN-based Internet of Things networks. The effectiveness of the proposed solutions is evaluated through extensive simulations across diverse deployment scenarios. The results demonstrate that the proposed MAC protocol achieves a throughput of 350 bps, outperforming conventional protocols that typically reach only 220 bps. Latency is reduced to 50 ms from 85 ms, energy consumption is decreased to 2.5 joules from 4.5 joules, and the packet delivery ratio (PDR) is improved to 95%, compared to 75% in existing approaches. These findings highlight the potential of the proposed protocol and routing scheme to significantly enhance the performance, energy efficiency, and sustainability of LoRaWAN-based IoT networks.
Volume: 16
Issue: 3
Page: 1626-1644
Publish at: 2026-06-01

Dynamic portfolio optimization using differential evolution: a Markowitz modern portfolio theory approach

10.11591/ijai.v15.i3.pp2449-2458
Hengki Tamando Sihotang , Jonson Manurung , Bambang Saras Yulistiawan , Galih Prakoso Rizky A.
An optimal investment portfolio is one of the main focuses in the financial world to minimize risk while maximizing returns. However, the challenge that arises is how to choose the right asset allocation amidst dynamic market uncertainty. This study aims to optimize portfolios based on Markowitz modern portfolio theory (MPT) by using the differential evolution (DE) algorithm as an optimization technique. The data used includes stocks, bonds, and other financial instruments taken from trusted data sources, such as Bloomberg and Yahoo finance, with an observation period of the last five years. The results show that this approach succeeds in finding optimal portfolios with the right asset weights, higher expected returns, and minimized risks compared to conventional approaches. The implication of this research is that the DE algorithm can be effectively used to address portfolio optimization problems in complex and volatile market environments, offering a more adaptive solution for investors to maximize their returns.
Volume: 15
Issue: 3
Page: 2449-2458
Publish at: 2026-06-01

DriveShield: attention-based hybrid neural network for intrusion detection in automotive controller area networks

10.11591/ijai.v15.i3.pp2618-2632
Vismaya Kootayi Kunnacheri , Arul Leena Rose Peter Joseph
Vehicle network security is important as increasing amounts of connected technology are being added to vehicles nowadays, putting them at risk of cyberattacks. This paper presents DriveShield, a novel real-time intrusion detection system (IDS) that is the first to combine gated recurrent units (GRU), convolutional neural networks (CNN), and long short-term memory (LSTM) with an attention mechanism. The systematic pre-processing pipeline, which includes feature engineering, the synthetic minority oversampling technique (SMOTE) for class balancing, and normalization. The model was validated on the open training intrusion detection system (OTIDS) dataset and the Hacking and Countermeasure Research Lab (HCRL) car hacking dataset. In the HCRL dataset, the model had an accuracy of 96.30% with F1-scores as high as 96% for all kinds of attacks. On the OTIDS dataset, it performed very well in terms of generalization, with a highest accuracy of 99.78% and a weighted F1-score of 99.78%. The addition of an attention mechanism enabled the model to concentrate on the most significant features, providing better adaptability to changing threats. These findings demonstrate the efficacy, scalability, and reliability of the system for in-vehicle network security. The future research will focus on performance on lower-frequency attacks through the study of unsupervised learning methods and real-world deployment trials.
Volume: 15
Issue: 3
Page: 2618-2632
Publish at: 2026-06-01

Flashover of a polluted high voltage insulator under electric field distribution

10.11591/ijece.v16i3.pp1097-1106
Zainab Abdullah , Izham Zainal Abidin , Miszaina Osman , Nurulazmi Abd. Rahman , Muhammad Shafiq
This study investigates the effect of surface pollution on a single-unit 11 kV glass suspension insulator using two-dimensional (2D) axisymmetric simulations in COMSOL Multiphysics. The developed model incorporates the electrical properties of glass, cement, steel electrodes, surrounding air, and a uniform pollution layer, with an applied AC voltage of 11 kV under quasi-static conditions. Simulation results demonstrate pronounced electric field intensification in the polluted configuration, particularly at the air–glass–cap triple junction region, where localized electrical stress is significantly higher compared to the clean condition. While the clean insulator operates within IEC 60383 recommended limits, the polluted model exhibits elevated peak electric field magnitudes, indicating increased flashover vulnerability. The findings highlight the strong influence of surface contamination, material permittivity, and geometric configuration on electric field distribution along the creepage path. This study establishes a reliable and computationally efficient predictive framework for optimizing insulator design, improving maintenance strategies, and enhancing the long-term reliability of high-voltage transmission systems, especially in pollution-prone environments.
Volume: 16
Issue: 3
Page: 1097-1106
Publish at: 2026-06-01

Sub-X-band reconfigurable antenna network with graphene slots

10.11591/ijece.v16i3.pp1249-1260
Hassna Agoumi , Seddik Bri , Youssef El Amraoui , Adil Saadi
This paper presents the design and analysis of a graphene-slotted hexagonal microstrip patch antenna and its extension to a compact 4×4 planar array operating in the sub-X-band. The objective of this work is to demonstrate that graphene-based electrical reconfigurability can be extended from a single antenna element to an array configuration while improving radiation performance. The proposed antenna integrates graphene slots etched into the radiating patch, where reconfigurability is achieved by electrically tuning the graphene conductivity through an external gate voltage Vg. The single antenna operates around 9.4 GHz with an impedance bandwidth of 400 MHz and a peak gain of 6 dB. The design is then extended to a 4×4 array with an inter-element spacing of approximately 1.2 wavelengths. The array operates in the 9–10 GHz range, provides a bandwidth of 380 MHz, and achieves a maximum gain of 13.08 dB. The results confirm that graphene-enabled reconfigurability can be preserved at the array level without increasing structural complexity.
Volume: 16
Issue: 3
Page: 1249-1260
Publish at: 2026-06-01

A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control

10.11591/ijece.v16i3.pp1531-1542
Joni Fat , Parwadi Moengin , Pudji Astuti , Sally Cahyati
Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Volume: 16
Issue: 3
Page: 1531-1542
Publish at: 2026-06-01

Hybrid deep learning (ILeS-Net) for lung cancer classification in cloud-IoT healthcare systems

10.11591/ijece.v16i3.pp1588-1607
Affrose Affrose , Cheruku Sandesh Kumar , Archek Praveen Kumar
This study presents a cloud–Internet of Things (cloud-IoT) based intelligent decision support framework for lung cancer classification and treatment recommendation, centered on a hybrid deep learning model termed ILeS-Net. Computed tomography (CT) images from a benchmark dataset are first preprocessed using Gaussian filtering to enhance image quality. Cancerous regions are identified using an Improved BIRCH (I-BIRCH) segmentation approach, followed by feature extraction using shape descriptors, color features, and Improved local Gabor XOR pattern (I-LGXP) textures. The extracted features are classified using ILeS-Net, which integrates Improved LeNet-5 and SqueezeNet architectures to achieve improved classification performance with reduced overfitting. Based on the classification results, the framework provides supportive recommendations to assist clinical decision-making. Experimental results demonstrate that the proposed ILeS-Net model achieves a maximum accuracy of 0.951, outperforming several conventional and state-of-the-art methods. The cloud–IoT integration further enables scalable, real-time, and secure data processing, highlighting the framework’s potential for practical computer-aided lung cancer diagnosis.
Volume: 16
Issue: 3
Page: 1588-1607
Publish at: 2026-06-01

GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection

10.11591/ijece.v16i3.pp1307-1318
Saiful Islam , Md. Nasim Akhtar , M. Mahadi Hassan , A. N. M. Rezaul Karim , Israt Binteh Habib
Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection.
Volume: 16
Issue: 3
Page: 1307-1318
Publish at: 2026-06-01

A survey of retrieval algorithms in ad and content recommendation systems

10.11591/ijece.v16i3.pp1518-1530
Yu Zhao , Fang Liu , Yuan Yuan , Yifan Dang
This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
Volume: 16
Issue: 3
Page: 1518-1530
Publish at: 2026-06-01
1 2 3
...
2062 2063
Show 59 of 2063

Discover Our Library

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

Explore Now
Library 3D Ilustration