Journal of Applied Engineering and Technological Science (JAETS)
Journal of Applied Engineering and Technological Science (JAETS) is published by Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI), Pekanbaru, Indonesia. It is academic, online, open access, peer reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. Journal of Applied Engineering and Technological Science (JAETS) is published annually 2 times every June and Desember. E-ISSN : 2715-6079, P-ISSN : 2715-6087. https://www.scopus.com/sourceid/21101138522 Journal title Initials Frequency DOI Print ISSN Online ISSN Editor-in-chief Publisher Language Indexing Citation Analysis : Journal of Applied Engineering and Technological Science : (JAETS) : JAETS : 2 issues per year (December and June) : by with Prefix 10.37385(https://doi.org/10.37385/jaets) : 2715-6087 : 2715-6079 : Assoc. Prof. Dr. Muhammad Luthfi Hamzah, B.IT., M.Kom : Yayasan Riset dan Publikasi Intelektual (YRPI) : English (preffered) : : Scopus | <a href="https://doaj.org/toc/2715-6079?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22index.issn.exact%22%3A%5B%222715-6087%22%2C%222715-6079%22%5D%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22match_all%22%3A%7B%7D%7D%7D%7D%2C%22size%22%3A100%2C%22sort%22%3A%5B%7B%22created_date%22%3A%7B%22order%22%3A%22desc%22%7D%7D%5D%2C%22_source%22%3A%7B%7D%7D" target="_blank" rel="noopener">DOAJ | Garuda | Moraref | IndexCopernicus | WorldCat | DRJI | SCILIT | Dimensions| SINTA 1 | : Scopus | Web of Science | Google Scholar | Dimensions
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Early Detection of Foetal Pathological Conditions with Neural Network Method: Implementation of Backpropagation Neural Network and SMOTE on Cardiotocography Data
This research focuses on the development of an effective classification model for early detection of foetal pathological conditions using Cardiotocography (CTG) data by utilising the Backpropagation Neural Network (BPNN) method. The high maternal mortality rate (MMR) and infant mortality rate (IMR) in Indonesia, including Riau Province, emphasise the importance of accurate prenatal diagnosis. The main challenge of this research is to address the class imbalance issue in the CTG dataset, which is biased towards the Normal class (77.9%) compared to the Suspect (13.9%) and Pathological (8.2%) classes. This problem was addressed by applying the Synthetic Minority Oversampling Technique (SMOTE). The model's performance was evaluated using K-Fold Cross Validation (5-Fold and 10-Fold). The test results showed that the combination of BPNN and SMOTE significantly improved performance, achieving a highest average accuracy of 92.66% and a maximum accuracy of 94.84% in the 10-Fold Cross Validation scheme. The resulting model is stable, has a high generalisation capability, and has great potential to be integrated into an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) to support evidence-based health policies in reducing Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR).
Copyright (c) 2026 Elin Haerani, Fadhilah Syafria, Novriyanto Novriyanto, Ismail Marzuki
Human Brain Tumors Detected by A Deep Learning Method Through a Pre-Trained Model
As a result, abnormal cells develop in the body, leading to a highly constitutive cell type that is a significant risk to the patient's functional capabilities and vital processes. The early and accurate recognition of such cells is crucial for accurate diagnosis and prognosis, and this recognition is made possible by medical imaging techniques, particularly magnetic resonance imaging (MRI). Despite advances in 3D learning models, several scientific studies involving deep convolutional networks (CNNs) still face numerous challenges. These challenges include the underutilization of spatial information, the inability of traditional data reduction techniques to minimise data dimensionality during the assembly phase, and suboptimal data processing during the data synchronisation or listening. In addition, some approaches require large volumes of data to achieve sufficient performance, which limits their applicability to real-world healthcare scenarios. This paper discusses the V-Net model that has been trained for a relatively long time to process volumetric 3D data, including a wide variety of very small sub-3D spatial volumes. This work used a large global MRI dataset, split into 80% for the training set and 20% for the test set. Before the tests, the images were preprocessed by resizing them to 128 × 128, applying Min-Max normalisation, and CLAHE (Contrast Limited Adaptive Histogram Equalisation) to enhance contrastof the images. The results showed that the proposed model achieved a 99% improvement in tumour detection performance over all other approaches. The findings indicate that employing specialised architectures like V-Net may significantly enhance the efficiency of medical diagnostic imaging specialists.
Copyright (c) 2026 Hanan H. Al-Nidawi, Farah K. AL-Jibory, Mohammed S. Hamid, Ruaa S. Salman
The Optimisation of Technological Parameters of Centrifugal Impact Crushers for Grain Grinding
This study aims to increase the energy efficiency and product uniformity of centrifugal impact crushers for feed grain processing through targeted optimisation of technological parameters. A direct-impact mechanism was implemented by profiling the stationary impact lining with an involute tooth geometry that ensures grain collisions at a 90° angle, minimising kinetic energy losses. Experimental tests were conducted on pea grain (moisture 11.6%) using a prototype crusher developed at Kostanay Regional University. Rotor speed was set at 2000 rpm, throughput maintained at 400 kg/h. The optimised crusher achieved a specific energy consumption of 2.89 kWh/t, which is 22% lower than a conventional impact crusher of similar capacity. The crushed product had an arithmetic mean particle size of 1.31 mm with 85% of particles within the 0.5-1.5 mm range, meeting zootechnical standards. Theoretically, this work refines comminution models by quantifying the effect of impact angle on breakage efficiency and provides a design criterion (involute profile with 47°-56° contact angles) for normal-impact crushers. Practically, the prototype offers a low-energy, high-uniformity solution suitable for feed mills and can be retrofitted into existing machines. The main contribution is the experimental validation that deliberate alignment of impact geometry reduces specific energy by over one-fifth without sacrificing throughput or quality, opening a new direction for energy-efficient grinder design.
Copyright (c) 2026 Natalya Kamysheva, Ayap Kurmanov, Vladimir Sapa, Serik Akhanov
Linking Mercury Contamination to Transport Dynamics in an Indonesian River: A Data-Driven Engineering Framework for ASGM-Impacted Watersheds
While Artisanal and Small-Scale Gold Mining (ASGM) severely contaminates watersheds with mercury (Hg), existing studies primarily diagnose pollution levels without identifying the underlying transport mechanisms or actionable engineering solutions. Addressing this gap, this study analyzes Hg concentrations, identifies physical transport vectors, and proposes a data-driven mitigation framework for the Kuantan River, Indonesia. A targeted spatial sampling (n=10) was conducted during the dry season (June 2025), with water samples analyzed using Cold Vapour Atomic Absorption Spectrometry (CVAAS). Results revealed gross contamination, with 100% of samples exceeding the World Health Organization (WHO) limit of 0.001 mg/L (ranging from 0.0027 to 0.0081 mg/L). The Heavy Metal Toxicity Load (HMTL) indicated critical toxicological risks (3.94–11.81). Crucially, Principal Component Analysis (PCA) identified Total Dissolved Solids (TDS) as the dominant spatial transport vector, demonstrating that Hg is predominantly particulate-bound rather than dissolved. To mitigate this, a hierarchical engineering framework is proposed, featuring source control (mercury-capturing retorts), pathway interruption (sedimentation basins to trap TDS), and receptor protection (point-of-use filtration). Although limited by a small sample size, this study extends foundational environmental engineering knowledge by linking statistical transport diagnostics to structural interventions, offering a replicable policy and watershed management blueprint for ASGM-impacted regions globally.
Copyright (c) 2026 Rindi Genesa Hatika, Indang Dewata, Ria Karno, Ika Daruwati, Fadhila Hidayah
Noise Reduction of Motion and EMG Artifacts in Holter ECG Using IIR Filters for Robust Arrhythmia Detection
Ambulatory Holter electrocardiography (ECG) enables continuous monitoring for detecting transient arrhythmias; however, its diagnostic reliability is significantly degraded by motion artifacts and electromyographic (EMG) interference. Under severe motion artifact conditions, prior studies report that ambulatory ECG SNR can fall below −10 dB , although SNR levels vary substantially depending on activity type and electrode placement, reducing usable data segments and impairing arrhythmia detection. While advanced denoising methods such as wavelet transforms and deep learning achieve high accuracy, their computational complexity limits real-time deployment in resource-constrained embedded systems. This reveals a critical gap in lightweight methods that jointly optimize noise suppression, morphological preservation, and downstream diagnostic performance. This study proposes a computationally efficient IIR Butterworth bandpass filtering framework for real-time IoT-based Holter ECG systems. The system combines three-lead ECG acquisition, embedded processing on an ESP32, and real-time visualization. Performance is assessed using SNR, mean squared error (MSE), Pearson correlation, and confusion matrix-based detection metrics on ten male participants under controlled motion and muscle artifact conditions. Results demonstrate statistically significant SNR improvements for motion artifacts (ΔSNR = 9.47 ± 1.96 dB, t(9) = 15.28, p < 0.001) and EMG artifacts (ΔSNR = 16.73 ± 0.91 dB, t(9) = 58.11, p < 0.0001). Post-filtering morphological fidelity was high, with mean Pearson correlation of 0.963 for motion artifacts and 0.945 for muscle artifacts. These signal quality improvements translated into 95.3% post-filtering arrhythmia detection accuracy (sensitivity: ≈96.0%, specificity: ≥97.0%, F1-score: ≥95.0%), significantly exceeding the 70% minimum performance threshold adopted in this study as a conservative screening criterion (t(9) = 29.7, p < 0.001). Despite dataset limitations (n = 10), the proposed framework provides an effective trade-off between computational efficiency and diagnostic reliability, supporting scalable and real-time ambulatory ECG monitoring for early arrhythmia screening.
Copyright (c) 2026 Sumber Sumber, Endang Dian S, Triwiyanto Triwiyanto, Roichatun Nashichah, Vijay Anant Athavale
Pose Estimation Frameworks in Healthcare: A Systematic Review
Human pose estimation has become increasingly important in healthcare applications such as fall detection, gait analysis, and rehabilitation monitoring. However, existing systematic reviews remain fragmented and largely descriptive, with limited comparative benchmarking and insufficient attention to clinical validation. This study addresses this gap by providing a structured comparison of major pose estimation frameworks in healthcare contexts. A systematic literature review was conducted using the PICOC framework and PRISMA guidelines. Studies published between 2020 and 2025 were retrieved from Scopus, Web of Science, IEEE Xplore, and PubMed based on predefined inclusion and exclusion criteria. Following screening and quality assessment, 41 studies were included in the final analysis. The results indicate that framework performance varies according to application requirements. OpenPose offers high anatomical precision but requires substantial computational resources, whereas MoveNet and MediaPipe enable real-time performance with lower latency, making them suitable for mobile and telehealth settings. Nevertheless, the evidence remains heterogeneous, with challenges related to occlusion, lighting variability, lack of standardized datasets, and limited real-world clinical validation. This study contributes by providing a theoretical synthesis and practical guidance for selecting appropriate pose estimation frameworks in healthcare applications.
Copyright (c) 2026 Egga Asoka Asoka, Fathoni Fathoni, Hadipurnawan Satria, Indra Griha Tofik Isa
Adoption of Berastagi Verse in Mobile Metaverse Learning: Extending TAM With Immersion and Cultural Value
This study examines user acceptance of Berastagi Verse, a Roblox-based mobile metaverse learning platform that reconstructs tourism destinations in Berastagi, Indonesia, for cultural learning. The study extends the Technology Acceptance Model by adding immersive experience and cultural value as predictors of behavioral intention. A purposive sample of 180 respondents who used the platform on smartphones completed a questionnaire after interacting with the system. Data were analyzed using PLS-SEM. Perceived ease of use positively influenced perceived usefulness, and both constructs positively predicted behavioral intention. Immersive experience and cultural value also had significant direct effects on intention. The model explained 64.2% of the variance in behavioral intention, suggesting meaningful explanatory power for this context. The findings indicate that mobile metaverse learning is adopted not only for usability and usefulness, but also for the extent to which it feels immersive and culturally relevant. For educational designers, the results suggest that mobile learning platforms should combine intuitive interaction, clear pedagogical value, and culturally authentic content when seeking user adoption.
Copyright (c) 2026 Rian Farta Wijaya, Randi Rian Putra
DAM Price-Based Model Predictive Control for Smart EV Charging under Grid and User Constraints
The rapid deployment of Electric Vehicles (EVs) has significantly increased grid congestion, particularly in regions with limited capacity for infrastructure expansion where system operators no longer permit customers to extend grid connections. Dynamic energy pricing has emerged to incentivize consumers to optimize energy use through time-of-day tariffs. However, existing smart charging approaches typically optimize grid constraints, cost, or user preferences in isolation, with limited integration of these objectives. This paper proposes a cloud-based Model Predictive Control (MPC) framework for smart EV charging that simultaneously enforces grid power limits, minimizes charging cost, and satisfies user-defined requirements. The proposed method incorporates day-ahead market (DAM) electricity prices, real-time building load, photovoltaic (PV) forecasts, and EV user inputs within a multi-objective optimization problem solved using a receding horizon strategy. The approach is validated through both simulation and a real-world deployment in a commercial building with multiple EV chargers. Results show that the proposed strategy achieves charging cost reductions of up to 95% under favorable overnight pricing conditions and up to 87% in real-world operation with grid constraints, while maintaining user satisfaction. The findings demonstrate the practical feasibility and contribution of an integrated, cloud-based MPC approach for scalable, cost-efficient, and grid-compliant EV charging.
Copyright (c) 2026 Sarab AL-Chlaihawi, Faris A. Alhaddad
VRACE-VANET : Fuzzy-based Relaible Adaptive Clustering Approach For Connectivity Enhancement
Vehicular Ad Hoc Networks (VANETs) play an important role in ensuring reliable communication in Intelligent Transportation Systems (ITS). This helps to improve efficient transportation services for vehicles. However, several existing clustering methods such as mobility based and weighted clustering algorithms, which face challenges in maintaining stability in clusters. This issue is further pronounced in environments where there is high vehicle mobility and periodic changes in network structure. Therefore, to overcome these drawbacks, this study proposes Vehicular Reliable Adaptive Clustering Environment (VRACE), an adaptive clustering method based on a fuzzy approach. This incorporates queuing theory to improve the cluster stability and communication efficiency of the network. This method selects the cluster heads based on several factors such as relative mobility, direction of vehicles, link quality, travel direction and vehicle speed. Estimating these factors allows the structure to make adaptive decisions suitable for dynamic vehicular environments. This system was evaluated through simulation under different vehicle density scenarios using SUMO and NS2. The proposed method improves overall network performance by showing approximately 14% increase in cluster lifetime, 2.5% higher throughput, 4.3% improvement in packet delivery ratio (PDR) and 22.5% reduction in end-to-end delay. These findings indicate that VRACE can support reliable communication in dense and rapidly changing vehicular networks.
Copyright (c) 2026 J. Naskath, Subir Gupta, Duc-Tan Tran, Nguyen Canh Minh
Backpropagation Artificial Neural Network For Classification Arrhythmia in ECG Signals
Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, accounting for approximately 17.9 million deaths annually. Among these, arrhythmias represent a significant concern due to their potential to lead to severe cardiac events. Traditional methods for detecting arrhythmias often require specialized equipment and healthcare facilities, which may not be readily accessible, especially in remote areas. This paper proposes the development of a portable electrocardiogram (ECG) device integrated with an Artificial Neural Network (ANN) using the Backpropagation algorithm to classify arrhythmias, thereby facilitating early detection and management. Arrhythmia is a heart condition characterized by an irregular heartbeat, where the heart may beat faster or slower than normal. Classification of arrhythmia can assist patients in monitoring their heart condition without needing to visit the hospital. This final project implements the Artificial Neural Network (ANN) method due to its ability to perform fast and accurate classifications. Prior to classification, feature extraction is carried out to detect the R wave interval, T wave interval, and the differences between the R and T wave intervals. The classification results are then displayed through a graphical user interface (GUI). The development of this ANN-based arrhythmia signal classification tool aims to help patients detect heart abnormalities at an early stage, potentially preventing the condition from worsening. Testing was conducted on 11 individuals, with 9 identified as having normal heart signals and 2 diagnosed with arrhythmia. When compared to a simulator, the classification system achieved 100% accuracy.
Copyright (c) 2026 Nurista Wahyu Kirana, Ervin Masita Dewi, Vanesha Putri Anggita, Yana Sudarsa, Dodi Budiman Margana, Sugondo Hadiyoso
Operational AI Opportunity Formation in Small Industry: A TOE-DOI Explanation
Artificial intelligence (AI) is increasingly accessible to small firms, but prior studies mainly explain readiness, adoption, or downstream value rather than the stage at which AI first becomes visible as an operational opportunity. This study examines that upstream stage in small industrial firms by combining technology-organization-environment (TOE) antecedents with diffusion of innovation (DOI) outcomes. A purposive, census-oriented survey was conducted on the official West Sumatra small-industry frame. The final dataset contains 51 usable responses from 69 registered small industrial firms, equal to 73.91% official-frame coverage, and was analyzed with PLS-SEM in SmartPLS 4. A conservative rerun removed one weak infrastructure indicator while retaining the same inner model. The main supported path is human resources and digital literacy to observability (β = 0.609, p = 0.001, f² = 0.318), followed by governance, strategy, and budget support to observability (β = 0.447, p = 0.030, f² = 0.179). Observability reaches R² = 0.496, whereas infrastructure, data management, competitive pressure, and ecosystem support do not show comparable direct effects. The findings indicate that early AI opportunity becomes visible mainly through interpretive workforce capacity and bounded managerial support.
Copyright (c) 2026 Ikhwan Arief, Prima Fithri, Angelina Ajeng Masayu, A Aisyah
Why Digital Capabilities Alone Don’t Drive SME Performance: The Mediating Role of Process Innovation in Retail SMEs
This study explores the role of digital capabilities (DC) in enhancing the business performance (BP) of retail SMEs in Johor, Malaysia, with a particular focus on the mediating effect of process innovation (PI) and the moderating role of competition intensity (CI). Despite the rising relevance of digital transformation, retail SMEs in Johor, Malaysia, continue to face structural constraints, including limited technological readiness, skill shortages, and intensified competitive pressures. Using primary data from 371 retail SMEs, this study employs Partial Least Squares Structural Equation Modelling (PLS-SEM) to test a multidimensional model grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT). The findings reveal that while DC do not exert a direct influence on BP, they significantly enhance PI, which in turn fully mediates the impact on performance. CI does not significantly moderate the relationship between DC and business outcomes. These results suggest that the BP benefits of DC are only realised through innovation-enabling mechanisms rather than direct technological adoption. The study underscores the importance of investing in internal innovation processes and strategic integration to translate digital potential into sustained competitive advantage within the retail SME context.
Copyright (c) 2026 Nurul Binti Samsuden, Alhamzah F. Abbas, Nor Amira Syairah Zulkarnain, Wai Chuen Poon, Ummu Ajrah Abdul Rauf
The Effect of Quenching With Oil on Annealing Temperature Variations on The Hardness Of ST 37 Equivalent Steel
ST37 equivalent steel is widely used in engineering applications due to its high ductility; however, its relatively low hardness limits its performance in wear-related components. This study investigates the effect of annealing temperature variations and different oil quenching media on the hardness of ST37 equivalent steel. Specimens were annealed at 750°C, 850°C, and 950°C with a holding time of 10 minutes, followed by quenching in three types of oil: SAE 15W-40 mineral oil, SAE 10W-40 semi-synthetic oil, and SAE 10W-40 fully synthetic oil. Hardness measurements were conducted using the Rockwell B scale (HRB) at five different points on each specimen. The results indicate a consistent increase in hardness with increasing annealing temperature for all quenching media. The highest hardness value, 43.4 HRB, was obtained at 950°C using fully synthetic oil, representing an improvement of approximately 4.58% compared to the untreated material. Among the tested media, fully synthetic oil exhibited the most effective cooling performance. These findings demonstrate that both annealing temperature and oil type significantly influence the hardness characteristics of ST37 equivalent steel.
Copyright (c) 2026 Muh Anhar, Yusuf Yusuf, Irianto SP, Ningrum Astriawati
Regarding the Features of Water Flow Interaction with a Rotor Turbine
The increasing demand for efficient energy production in Kazakhstan underscores the significance of optimising rotor turbines used in gravitational water vortex micro-hydroelectric power plants (MWV-MHPPs). This study addresses the challenges in rotor-flow interaction, focusing on improving turbine efficiency and mitigating performance errors. The research gap lies in the limited understanding of rotor dynamics in relation to varying water flow conditions, which directly impact turbine output. This study employs computational fluid dynamics (CFD) modelling and experimental testing to analyse the interaction between water flow and rotor turbines, focusing on key performance indicators such as torque, head loss, and flow characteristics under different operational conditions. The results indicate a 15% improvement in torque efficiency and a 10% reduction in head loss with optimised turbine blade design, compared to traditional configurations. The study also uncovers significant design flaws in existing MWV-MHPPs, contributing to energy inefficiencies in Kazakhstan’s renewable energy sector. This work proposes specific recommendations for turbine design, including enhanced blade geometry and flow regulation strategies, to mitigate these issues. The findings offer practical insights for the development of sustainable, off-grid energy solutions, contributing to the country’s energy security goals. By addressing the interaction between water flow and rotor turbines, this research advances the understanding of turbine optimisation for small-scale hydropower plants.
Copyright (c) 2026 Kazbek Orazbayev, Assel Yerkongyr, Rakhimash Abitayeva, Gulmira Sakhmetova, Balnur Shashubay
Current State of Innovation and Management Control, Ready Made Garments Industry Towards Circular Economy
This study investigates the impact of innovative strategies and management control systems (MCS) on the implementation of circular economy (CE) practices within Bangladesh’s ready-made garment (RMG) sector. Utilizing the Resource-Based View (RBV), Natural Resource-Based View (NRBV), and Stakeholder Resource-Based View (SRBV), the study constructs a comprehensive framework that differentiates between internal innovation capabilities and externally acquired knowledge. Data were gathered from 142 management-level professionals using a standardized questionnaire and analyzed using PLS-SEM. The results indicate that open innovation and management control systems significantly enhance CE adoption, but green product and process innovation have favorable yet statistically negligible correlations. Moreover, MCS does not substantially influence the link between innovation and CE. The results indicate that, in buyer-driven and compliance-intensive sectors, the adoption of a circular economy is predominantly influenced by institutional factors rather than by organizational capabilities. The research enhances theoretical understanding by illustrating the contextual constraints of NRBV in low-autonomy supply chain settings and emphasizing the preeminent influence of external collaboration and compliance-driven governance. The findings indicate that organizations ought to prioritize strategic relationships and organized control systems rather than solitary internal innovation efforts to expedite circular transformation.
Copyright (c) 2026 Mohammed Masum Billah, Mohd Helmi Ali, Syed Shah Alam, Mazzlida Mat Deli
Implementation of an Electromagnetic Induction Coil System Integrated With PLC Using an Adaptive Control Method for Enhanced Energy Efficiency
Electromagnetic induction heating is widely used in manufacturing due to its fast and localized heating capability. However, conventional constant-power and fixed PID control methods often struggle with nonlinear and time-varying thermal dynamics, leading to temperature overshoot, long settling times, and inefficient energy use. These limitations highlight the need for adaptive and energy-efficient control strategies, especially in PLC-based industrial systems. This study proposes a PLC-based adaptive control framework using a self-tuning PID algorithm, where control parameters are automatically adjusted in real time based on temperature error and system response. The method enables continuous adaptation to improve thermal tracking under dynamic conditions. Experimental validation was performed by heating workpieces to 600 °C and evaluating performance using rise time, settling time, overshoot, steady-state error, and energy consumption. Compared to a conventional constant-power method, the proposed approach shows significant improvements in transient and steady-state performance, with reduced rise time, settling time, and overshoot. Additionally, energy consumption decreased from 1.6067 kWh to 1.3265 kWh, representing a 17.44% improvement. The integration of PLC enhances real-time system responsiveness and heat uniformity. Overall, the proposed method effectively bridges the gap between fixed control and adaptive, high-performance thermal regulation for Industry 4.0 applications
Copyright (c) 2026 Gun Gun Maulana, Abdur Rohman Harits Martawireja, Nur Wisma Nugraha
Flood Prone Areas Mapping using Remote Sensing and GIS Techniques in Kalal Badrah Basin, Wasit, East of Iraq
Floods are considered one of the most dangerous natural disasters globally due to the damage they cause, impacting lives and damaging property, infrastructure, and economic activities. Despite the increasing frequency of flash floods in Iraq, particularly in the eastern regions, as a result of climate change, high-resolution spatial assessments remain limited. This study addresses this research gap by integrating the latest Landsat 9 satellite imagery and rainfall data from the Global Precipitation Measurement System (GPM IMERG) within a multi-criteria framework based on geographic information systems (GIS) for the Kalal Badra Basin. The scientific contribution lies in providing a local sensitivity map with a 30-meter resolution that classifies the basin into three risk zones, offering a vital tool for disaster mitigation in data-poor semi-arid environments. The study guides methodology based on DEM SRTM, Landsat image, GPM data. The approach described integrates remote sensing and GIS techniques. Various thematic layers such as slope, elevation, Normalized Difference Vegetation Index (NDVI), drainage density and precipitation were employed to delineate flood prone zones in the Kalal Badrah watershed in Iraq. The individual flood susceptibility maps of each thematic layer were combined with equal weights within the GIS environment to generate the overall flood susceptibility map of the study area. The final map classified the study area to three classes: No flood risk, low flood risk, and high flood risk area according to the selected criteria.
Copyright (c) 2026 Athraa Abbas Kadhim, Marwa Razzaq Aya, Zainab N. Jasim
Exploring Cloud-Based e-Learning Adoption in Developing Nations: A Comprehensive Review
Digital transformation in the form of Cloud-based e-learning (CBEL) in higher education has enabled anytime, anywhere learning environments. The literature on the adoption models for CBEL from developing nation’s perspectives is scarce. A current study presents a systematic literature review of CBEL adoption in developing nations. The PRISMA framework was used for the study. It articulates 27 different articles published during 2019-2025. The articles were chosen from Scopus and WoS databases. The review presents user intention to adopt to CBEL as high, but actual adoption depends on a number of contextual factors such as infrastructure, connectivity, training, support, security and policy. The review further reveals a strong reliance on user-level models such as TAM and UTAUT, with limited integration of organizational perspectives and reliance on perceptual measures over actual outcomes. Consequently, this study proposes an integrated framework combining technological readiness, organizational support, user acceptance, and adoption outcomes. Theoretically, CBEL adoption in developing countries cannot be explained only through user acceptance factors but also by organizational and infrastructural conditions. From a practical perspective, HEIs administrators and policymakers should view CBEL as a sociotechnical system, not only a digital tool. Future research should focus on longitudinal empirical validation of the proposed framework.
Copyright (c) 2026 Khalilullah Khalid, Rajashree Jain
Intelligent Wearable Technology For Hajj: AI-Powered Smart Bracelet for Real-Time Pilgrim Management
The Hajj pilgrimage gathers millions of people each year, creating a dynamic environment that demands efficient health monitoring, safety management, and real-time coordination. This paper presents an AI-powered smart bracelet designed to enhance pilgrim management through the integration of Internet of Things (IoT) technology, fuzzy logic intelligence, and cloud computing. The wearable continuously collects physiological and environmental data—such as temperature, heart rate, humidity, and location—and processes them through a hybrid edge–cloud framework to ensure low latency and scalability. Artificial intelligence algorithms analyze sensor data to detect anomalies and issue early alerts during health or safety incidents. Experimental evaluation under simulated Hajj conditions achieved an average classification accuracy of 94.8%, data transmission latency below 3.5 seconds, and battery endurance of up to 20 hours. These results confirm the system’s reliability, energy efficiency, and suitability for large-scale real-time monitoring. The proposed framework contributes to safer and smarter Hajj operations by improving early risk detection, communication efficiency, and emergency response coordination.
Copyright (c) 2026 Rosalina Rosalina, Hasanul Fahmi, Noor Lees Binti Ismail, Danny Ngo Lung Yao
Vision Transformer for Active Compound Function Classification Based on 2D Molecular Structures
Accurate classification of active compounds based on molecular structure is crucial for accelerating drug discovery while reducing laboratory costs and time. However, existing structure-based classification methods, particularly convolutional neural networks and graph-based models, often struggle to capture long-range dependencies or require large-scale datasets and extensive feature engineering. This study investigates the use of the Vision Transformer (ViT) model to classify 2D molecular structure images of compounds into cancer and cardiovascular therapy categories. A dataset containing 500 images, consisting of 250 per class, was obtained from the PubChem database, processed for consistency, and divided into 72% training, 20% testing, and 8% validation. To address the limited dataset size, careful preprocessing, regularization through weight decay, and systematic hyperparameter tuning were applied to reduce overfitting risks. The ViT model was trained with the Adam optimizer and a linear learning rate scheduler. Hyperparameters were systematically tuned to identify the optimal configuration. Results show that the best settings, with batch size 60, weight decay 0.1, learning rate 3.0×10⁻⁶, and 15 epochs, achieve an accuracy, F1 score, and loss of 80.0%, 79.9%, and 0.597, sequentially. These findings highlight the potential of ViT for small-scale cheminformatics tasks, offering an alternative to conventional methods while maintaining competitive performance.
Copyright (c) 2026 Dian Eka Ratnawati, Diva Kurnianingtyas, Agus Wahyu Widodo, Rekyan Regasari Mardi Putri
Enhancing efficiency in construction scheduling for SMEs: A novel matrix-based VBA-excel model
Small and medium-sized enterprises (SMEs) in construction face challenges managing complex project schedules cost-effectively. This study develops a user-friendly, matrix-based VBA-Excel model to enhance scheduling efficiency. The objective is to create an accessible tool that automates task dependency management without requiring advanced coding skills. The model employs the Precedence Diagramming Method (PDM), integrating all dependency types (FS, SS, FF, SF) with lag/lead adjustments through VBA automation in Excel. Implementation involved designing a novel matrix structure for dependency handling and a simple interface, validated via case studies with 11 and 9 tasks, benchmarked against industry standards. Results show accurate schedule calculations matching benchmarks, with seamless adjustments, though processing slows for large projects. The tool enables SMEs to optimize resources, reduce delays, and improve competitiveness at no cost. Limitations include Excel’s computational limits and omission of real-world calendar constraints like holidays. Future enhancements could include cloud-based scalability and risk integration. This innovative model fosters inclusive project management, reducing infrastructure disparities in underserved regions.
Copyright (c) 2026 L. Nguyen-Son, T. Thai-Phuong
Selection of Strategic Pathways for the Comprehensive Development of Peru through the Analytical Hierarchy Process (AHP)
This study prioritizes strategic pathways for Peru’s comprehensive development using the Analytic Hierarchy Process (AHP). It addresses a methodological gap in development planning research by applying a unified multicriteria framework to compare heterogeneous national policy pathways that are often assessed separately. Four alternatives were evaluated: science and technology, tourism, infrastructure, and anemia reduction. The decision model included four criteria—economic impact, social impact, sustainability, and feasibility—based on pairwise judgments from a multidisciplinary panel of four experts in economics, civil engineering, public health, and sustainable tourism. The results showed that economic impact was the dominant criterion (0.518), followed by social impact (0.255), feasibility (0.169), and sustainability (0.057). The criteria matrix achieved acceptable consistency (CR = 0.047). After aggregating local and global priorities, science and technology ranked first (0.366; 36.6%), followed by tourism (0.270; 27.0%), infrastructure (0.243; 24.3%), and anemia reduction (0.122; 12.2%). A sensitivity analysis showed that the ranking remained stable under moderate variations in criterion weights. The study provides a transparent and replicable AHP-based framework for intersectoral prioritization and a practical analytical tool for development policy discussion in Peru.
Copyright (c) 2026 Hugo Flor-Cunza, Jandra Rojas Chavez, Antony De la Cruz-Vasquez, Pedro Sanchez-Huapaya, Linett Velasquez-Jimenez
A Parallel Comparative Multi-Scenario Framework For Diabetic Retinopathy Detection Using Three-Tiered Feature Selection
Early detection of Microaneurysms (MAs) is vital for diagnosing Diabetic Retinopathy, yet standard deep learning models often struggle with high false-negative rates and overfitting on limited medical datasets. Objective: This study proposes a Parallel Comparative Multi-Scenario Framework to identify the most robust configuration for MA detection. The framework evaluates independent 1D vectorized feature descriptors, each initialized as a high-dimensional 16,384-feature baseline, to avoid the redundancy inherent in feature fusion. Methodology: The system systematically processes six independent descriptors LBP, GLCM, Gabor, Wavelet, Fractal, and LMR across three selection tiers (Filter, Wrapper/RFE, and Embedded). These optimized vectors, reduced from the initial 16,384 dimensions to the most discriminative "Best Subsets," serve as uniform inputs for six classifiers: five traditional Machine Learning (ML) models and a proposed representation-consistent 1D-CNN architecture, resulting in 128 experimental scenarios. Results: Experimental evaluation was conducted on a balanced dataset of 740 fundus images derived from two distinct sources: the publicly available MESSIDOR dataset and a clinically acquired dataset from Hospital Universiti Sains Malaysia (HUSM). The model was trained on MESSIDOR data and subsequently evaluated on an independent HUSM test set to assess generalization performance. The results reveal a significant performance gap. The independent LBP-RFE-SVM scenario achieved the highest performance with an accuracy, recall, and precision of 91.00%. In contrast, the best Deep Learning (DL) configuration, Gabor-ANOVA-1DCNN, reached 87.00% accuracy. Notably, while the 1D-CNN maintained a "performance floor" of 60%, ML demonstrated extreme volatility, dropping to 51.00% with global statistical features. The optimal framework significantly minimized the False Negative Rate (FNR) to 6.76%, missing only 5 out of 74 cases.
Copyright (c) 2026 Loneli Costaner, Nor Hazlyna Harun
Analysis of the Use of Artificial Intelligence in the Form of a Chatbot as an Information Search Engine
The quick development of artificial intelligence has contributed to the use of chatbots as alternative methods for collecting information within organizations. However, empirical studies that combine technology acceptance and system success perspectives to clarify chatbot usage remain lacking. This study analyzes the adoption of chatbots as information search engines by integrating the Technology Acceptance Model (TAM) and the DeLone and McLean Information Systems Success Model (ISSM) through a quantitative approach involving the distribution of questionnaires to 400 employees who have experience interacting with chatbots. The research model includes Information Quality, System Quality, Service Quality, Perceived Ease of Use, Perceived Usefulness, Intention to Use, and Actual Use, and the data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that System Quality and Service Quality substantially impact Perceived Ease of Use and Perceived Usefulness. Perceived Ease of Use significantly influences Perceived Usefulness, which eventually impacts Intention to Use and Actual Use. However, Information Quality doesn't significantly impact Perceived Ease of Use or Perceived Usefulness. These results provide theoretical and practical insights for improving chatbot adoption for employees.
Copyright (c) 2026 Joshua Varrelino Rahardjo, Fathy Radhia, Edi Purnomo Putra
Development of Potato-Derived Carbon Nanofibers as Sustainable Moisture-Regulating Supporting Layers for Photovoltaic Modules
Moisture-induced degradation remains a critical reliability issue in photovoltaic (PV) modules, particularly in humid tropical climates. While biomass-derived carbon materials have been widely investigated for electrochemical applications, their use as functional supporting layers in PV modules remains limited. This study develops potato-derived carbon nanofibers through moderate-temperature carbonization (700 °C, N₂ atmosphere) followed by electrospinning. Structural and chemical properties were evaluated using XRD, FTIR, Raman spectroscopy, and BET surface area analysis. Morphology and fiber diameter distribution were examined via SEM. Electrical conductivity was measured using a four-point probe method. Water vapor permeability (WVP) and biodegradation behavior were assessed to evaluate durability and moisture regulation capability. The synthesized material exhibits predominantly amorphous turbostratic carbon with characteristic D and G Raman bands (ID/IG ≈ 0.92), specific surface area of 186 ± 12 m²/g, and electrical conductivity of 3.8 ± 0.4 S/m. Nanofiber diameters range from 72–108 nm (mean ± SD). Controlled WVP (3200–4100 mg/day/L) and moderate biodegradability (8–12% mass loss over 14 days) indicate balanced vapor diffusion and structural integrity. The results demonstrate the feasibility of potato-derived carbon nanofibers as sustainable moisture-regulating supporting layers in photovoltaic modules.
Copyright (c) 2026 Indri Dayana, Habib Satria, Nidya Chitraningrum, Mega Puspita Sari, Tino Hermanto, Yopan Rahmad Aldori, Dadan Ramdan, Syofyan Anwar Syahputra, Dina Maizana, Moranain Mungkin, Junaidi Junaidi, Ade Irma Sagala, Siti Utari Rahayu
A Non-Contact Height Measurement Method Using Mediapipe and Yolov8 in a Three-Dimension Space
Accurate human height measurement is an essential clinical parameter for calculating body mass index and assessing patient health status. However, traditional contact-based or manual methods are impractical for patients with limited mobility or during emergency conditions. This study addresses this limitation by developing a non-contact 3D height estimation system using a smartphone-based vision approach. The system employs MediaPipe to extract skeletal landmarks and a regression model to predict human height from bone segment lengths and a reference object. Experimental evaluation on 166 subjects across multiple positions demonstrates a high accuracy with an average error rate of 1.54 ± 0.64%, outperforming most existing camera-based systems. The proposed method offers a practical, low-cost, and portable solution for medical height assessment, particularly beneficial in clinical and emergency settings. Potential limitations include dependence on camera angle and lighting conditions, which will be addressed in future work.
Copyright (c) 2026 Phu Nguyen Trung, Dat Dong Quoc, Kien Nguyen Phan
Visual Detection of Oil Palm Maturity Leveraging Simple Evolving Connectionist System
Detecting the ripeness of oil palm fruit bunches is a crucial process in the palm oil industry to ensure the quality and quantity of oil extracted. Conventional methods still rely on subjective and inefficient manual observation. This study proposes a visual detection system using the Simple Evolving Connectionist System (SECoS) algorithm to identify the ripeness of oil palm bunches based on visual images. This model utilizes color, texture, and shape characteristics extracted from images and processed through an adaptive and evolving neural network structure. The results demonstrate that SECoS is capable of high detection accuracy and adapts to new data patterns. This system has the potential to be applied in precision agriculture practices. The model achieved an average accuracy of 91.3%, with the highest accuracy of 94% in the "Ripe" category in the final test based on 300 dataset. This demonstrates that parameter optimization is crucial in improving the model's ability to adapt to variations in oil palm bunch image data. Accuracy improvements were evident in both training and validation data. However, not all categories achieved optimal results, with accuracy for the "empty bunch" labels (89%) and "unripe" labels (88%) being relatively lower than for the other categories.
Copyright (c) 2026 Al-Khowarizmi Al-Khowarizmi, Fatma Sari Hutagalung, Halim Maulana
Enhancing Student Motivation in Programming Education Through N-EGM-Based Gamification: A Mobile Application Approach
Programming education continues to face persistent challenges, including high cognitive load, abstract syntax complexity, and declining intrinsic motivation among students. Although gamification has been widely adopted to address these issues, existing frameworks such as MDA and Octalysis lack structured personalization and socialization mechanisms tailored specifically for programming learning contexts. This study proposes a mobile-based programming learning application designed using the Newton Enhanced Gamification Model (N-EGM) and empirically evaluates its effectiveness through the Hedonic Motivation System Adoption Model (HMSAM). The study involved 116 undergraduate Informatics students selected using purposive sampling. Data were collected using validated questionnaire instruments and analyzed through descriptive statistics, reliability testing, multiple regression analysis, multicollinearity diagnostics, and common method bias detection using SPSS. The findings indicate that gamification elements mapped through the N-EGM framework explain 99.1% of the variance in student motivation and 98.9% of the variance in engagement (p < 0.001). Leaderboard and Objective elements were the strongest predictors of motivation, while Economy and Quest significantly influenced immersion. Multicollinearity diagnostics confirmed acceptable VIF values (< 5), and Harman’s single-factor test indicated no critical common method bias. Theoretically, this study contributes by integrating a structured multi-layer gamification framework with a hedonic adoption model in a programming education context. Practically, it provides a systematic design blueprint for implementing adaptive and socially integrated gamification strategies in mobile STEM learning environments.
Copyright (c) 2026 Ranty Deviana Siahaan, Boy Martahan Sitorus, Emely Angelica Lestari, Enrico Hezkiel Sirait
Utilization of Cocopeat, Empty Fruit Bunch, and Palm Kernel Shell as Renewable Energy Feedstock in Boiler
The increasing generation of biomass waste from coconut and palm oil industries presents both environmental challenges and opportunities for renewable energy utilization. This study evaluates the potential of cocopeat, empty fruit bunch (EFB), and palm kernel shell (PKS) as alternative fuels in boiler applications through fuel characterization, blending analysis, and thermochemical performance modeling. This research aims and objective to characterize the three wastes, determine the best composition mixture as boiler fuel, and estimate the potential exhaust gas emissions. This research uses laboratory testing methods, calculation of calorific value, and estimation of exhaust gas emissions (CO₂, SO₂, NO₂) using stoichiometric calculations and steam production modeling based on energy balance principles. The test was carried out by comparing cocopeat pellets (PECO); a mixture of 50% cocopeat and 50% EFB (BCO); PKS; and EFB. The results showed that 10% PECO, 30% EFB, 60% PKS has a high calorific value of 17.05 MJ/kg. Furthermore, NO2 emissions and steam production rate are decreased to 3.42% and 7.93% than 20% PECO, 40% EFB, 40% PKS. This is due to the high value of the coconut shell fraction (PKS), which produces a high calorific value. Furthermore, the cocopeat mixture, consisting of 10%BCO, 30%EFB, and 60%PKS, has low NO2 emissions and can produce high steam in boilers. This indicates that cocopeat can be used as a new fuel when mixed with EFB, thereby maximizing the utilization of coconut waste and reducing environmental impact.
Copyright (c) 2026 Annisa Bhikuning, Supriyadi Supriyadi, Sandi Apriandi Setiawan, Yustika Agustin, Suhaila Binti Hussain
Spatio-Temporal Graph Neural Network Based on Nonlinear Time–Frequency Features for Mu-ERD Classification in Multi-Session EEG Motor Imagery
Mu rhythm event-related desynchronization (ERD) is a key indicator of motor imagery activity based on EEG signals. However, accurate classification of ERD remains challenging due to the nonlinear nature of EEG signals and inter-session variability. This study proposes a motor imagery classification approach using a Spatio-Temporal Graph Neural Network (ST-GNN) model that leverages nonlinear time-frequency features extracted via Variational Mode Decomposition (VMD) and Synchrosqueezing Transform (SST). The dataset was collected from a single healthy subject across five separate sessions, each consisting of two conditions: relaxation and motor imagery. After preprocessing and segmentation, features were extracted and represented as spatio-temporal graphs to be processed by the ST-GNN. The model was evaluated using metrics such as accuracy, F1-score, AUC-ROC, and the Session Stability Index (SSI). The results show that the ST-GNN achieved an accuracy of 94.2%, F1-score of 94.1%, and AUC-ROC of 96.1%, along with high prediction stability across sessions. This performance outperformed baseline models including CNN, CSP+SVM, and STFT+MLP.These findings support the hypothesis that ERD is a distributed brain network phenomenon and demonstrate that the ST-GNN approach with VMD/SST-derived features is a promising strategy for developing adaptive and accurate BCI systems.
Copyright (c) 2026 Firman Aziz, Jeffry Jeffry, Syahrul Usman, Rahmat Fuadi Syam, Muhammad Nur Arafah, Nurul Fathanah Mustamin