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Journal of Applied Engineering and Technological Science (JAETS)
Published by Yayasan Pendidikan Riset dan Pengembangan Intelektual
ISSN : 27156087     EISSN : 27156079     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering
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.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 405 Documents
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Selection of Strategic Pathways for the Comprehensive Development of Peru through the Analytical Hierarchy Process (AHP) Hugo Flor-Cunza; Jandra Rojas Chavez; Antony De la Cruz-Vasquez; Pedro Sanchez-Huapaya; Linett Velasquez-Jimenez
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9213

Abstract

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.
Enhancing efficiency in construction scheduling for SMEs: A novel matrix-based VBA-excel model L. Nguyen-Son; T. Thai-Phuong
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9232

Abstract

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.
Integrating Mathematical Modeling and Deep Learning for Uncertainty-Aware Fault Diagnosis in Industrial Rotating Machinery Primawati Primawati; Ferra Yanuar; Dodi Devianto; Remon Lapisa; Fazrol Rozi; Arda Yunianta
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/a6wnmz27

Abstract

In Industry 4.0, reliable fault diagnosis is critical for minimizing downtime and preventing catastrophic failures in rotating machinery. However, conventional deep learning models often operate deterministically, lacking the ability to quantify prediction uncertainty—a limitation that hinders risk-based maintenance decisions. This study aims to develop a hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bayesian inference for uncertainty-aware fault diagnosis. The model extracts spatial features from Short-Time Fourier Transform (STFT) spectrograms via CNN, models temporal dynamics from raw vibration signals via LSTM, and quantifies prediction uncertainty using Monte Carlo Dropout (T=50). Evaluated on the benchmark Case Western Reserve University (CWRU) bearing dataset with an 80/20 data partitioning under six operating conditions, the hybrid architecture achieves an accuracy of 99.14% and an F1-score of 0.9914, significantly outperforming standalone CNN (97.42%) and LSTM (84.12%) models. The integration of probabilistic inference enhances decision reliability by providing confidence estimates for each prediction. This work contributes a robust, uncertainty-aware model that effectively captures both spatial and temporal patterns, offering significant implications for safety-critical industrial predictive maintenance systems.
Human Brain Tumors Detected by A Deep Learning Method Through a Pre-Trained Model Hanan H. Al-Nidawi; Farah AL-Jibory; Mohammed S. Hamid; Ruaa S. Salman
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9383

Abstract

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.
Intelligent Wearable Technology For Hajj: AI-Powered Smart Bracelet for Real-Time Pilgrim Management Rosalina Rosalina; Hasanul Fahmi; Noor Lees Binti Ismail; Danny Ngo Lung Yao
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9391

Abstract

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.
Exploring Cloud-Based e-Learning Adoption in Developing Nations: A Comprehensive Review Khalilullah Khalid; Rajashree Jain
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/n8nrjb27

Abstract

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.
Vision Transformer for Active Compound Function Classification Based on 2D Molecular Structures Dian Eka Ratnawati; Diva Kurnianingtyas; Agus Wahyu Widodo; Rekyan Regasari Mardi Putri
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9418

Abstract

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.
Noise Reduction of Motion and EMG Artifacts in Holter ECG Using IIR Filters for Robust Arrhythmia Detection Sumber Sumber; Endang Dian S; Triwiyanto Triwiyanto; Roichatun Nashichah; Vijay Anant Athavale
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9426

Abstract

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.
VRACE-VANET : Fuzzy-based Relaible Adaptive Clustering Approach For Connectivity Enhancement J. Naskath; Subir Gupta; Duc-Tan Tran; Nguyen Canh Minh
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9444

Abstract

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.
Implementation of an Electromagnetic Induction Coil System Integrated With PLC Using an Adaptive Control Method for Enhanced Energy Efficiency Gun Gun Maulana; Abdur Rohman Harits Martawireja; Nur Wisma Nugraha
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/46e2qy88

Abstract

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

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