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Scientific Journal of Engineering Research
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31091725     DOI : https://doi.org/10.64539/sjer
Core Subject : Engineering,
Engineering
The Scientific Journal of Engineering Research (SJER) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal is committed to publishing high-quality articles in all fundamental and interdisciplinary areas of engineering, with a particular emphasis on advancements in Information Technology. It encourages submissions that explore emerging fields such as Machine Learning, Internet of Things (IoT), Deep Learning, Artificial Intelligence (AI), Blockchain, and Big Data, which are at the forefront of innovation and engineering transformation. SJER welcomes original research articles, review papers, and studies involving simulation and practical applications that contribute to advancements in engineering. It encourages research that integrates these technologies across various engineering disciplines. The scope of the journal includes, but is not limited to: Mechanical Engineering Electrical Engineering Electronic Engineering Civil Engineering Architectural Engineering Chemical Engineering Mechatronics and Robotics Computer Engineering Industrial Engineering Environmental Engineering Materials Engineering Energy Engineering All fields related to engineering By fostering innovation and bridging knowledge gaps, SJER aims to contribute to the development of sustainable and intelligent engineering systems for the modern era.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 54 Documents
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Isolation Forest–Based Intrusion Detection for Cyber-Physical Systems Oise, Godfrey Perfectson; Konyeha, Susan; Uloko, Felix Oshiorenoya; Pius, Kevin Chinedu; Eferoba–Idio, Enovwo; Edobor, Michael Uyiosa; Mintah, Evans; Ukpebor, Osahon; Sokoya, Oludare; Jessa, Tejiri
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.434

Abstract

Cyber-physical engineering systems (CPES) form the backbone of critical infrastructures such as power generation, industrial automation, and water treatment facilities. Because cyber intrusions in these environments can directly disrupt physical processes, reliable intrusion detection mechanisms are essential for maintaining operational safety and system resilience. However, many existing intrusion detection approaches rely on supervised learning techniques that require large volumes of labeled attack data, which are rarely available in real industrial environments. In addition, advanced detection methods often introduce significant computational overhead, limiting their practicality for deployment in resource-constrained cyber-physical systems. To address these challenges, this study proposes a one-class anomaly detection framework based on the Isolation Forest algorithm for monitoring cyber-physical engineering systems. The proposed approach learns the statistical distribution of normal operational behavior using multivariate sensor, actuator, and control signals, and identifies deviations from this learned pattern as potential cyber intrusions. The framework is evaluated using the Hardware-in-the-Loop–based Augmented Industrial Control System (HAI) Security Dataset, which provides realistic industrial process measurements under both normal and attack scenarios. Experimental results show that the model achieves overall accuracy (0.89) and strong performance in identifying normal operational states (F1-score = 0.94). However, attack detection shows moderate recall (0.48) but low precision (0.04) due to class imbalance and overlapping anomaly score distributions. These findings indicate that Isolation Forest serves as a computationally efficient baseline anomaly detection mechanism for real-time CPS monitoring, while highlighting the need for hybrid and temporally aware detection strategies to improve attack discrimination in industrial cyber-physical environments.
Artificial Intelligence–Driven Simulation Models for Industrial Accident Prevention in Chemical Process Engineering Acuña Acuña, Edwin Gerardo
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.405

Abstract

Industrial accidents in chemical process engineering continue to pose a significant issue despite the widespread use of Industry 4.0 technology and data-driven monitoring systems. Traditional safety frameworks often depend on either purely empirical machine learning models or deterministic first-principles simulations, creating a methodological split that constrains prediction reliability in uncommon, high-impact situations. This work bridges the structural gap by incorporating physics-informed artificial intelligence into a digital twin architecture for the avoidance of industrial accidents. A methodological framework driven by simulation was established, integrating first-principles process modeling, synthetic data generation with controlled fault injection, supervised and unsupervised learning, and reinforcement learning for safety-constrained optimization. Physics-based limitations were included into the learning aim to maintain thermodynamic and transport consistency. The model's performance was assessed using safety-oriented criteria, such as detection delay, false negative rate, resilience to sensor noise, and stability amid parametric uncertainty. Results demonstrate that physics-informed models significantly reduce detection latency and false negatives in accident precursor regimes compared to purely data-driven baselines. The integration of constraint-aware learning improves extrapolation stability under class imbalance conditions typical of industrial safety datasets. Furthermore, explainable AI mechanisms enhance interpretability and regulatory transparency. These findings indicate that AI-enhanced simulation models reconfigure accident prevention strategies by shifting from reactive threshold systems to proactive, mechanism-consistent risk anticipation frameworks applicable to safety-critical chemical processes.
Probabilistic Finite Element Analysis of Temperature-Dependent Corrosion in Oil and Gas Pipelines Nrior, Million Matthew; Nitonye, Samson; Adumene, Sidum; Orji, Charles Ugochukwu
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.427

Abstract

Marine pipeline systems are continually exposed to operating conditions that accelerate internal corrosion, posing risks to flow assurance and structural integrity. This study applies finite element modelling to evaluate the influence of operating temperature on corrosion progression and pipeline performance. The study addressed gaps in temperature-based corrosion propagation in a pipeline using ANSYS Design Modeler, meshing, and exporting for flow-corrosion modelling in ANSYS Fluent. A one-way coupling was established between Fluent and ANSYS Mechanical to assess the mechanical response under operating conditions. The base case at 62 °C showed a corrosion rate of 6.0 mm/year. To investigate the role of temperature, simulations were conducted at 30 °C, 50 °C, 62 °C, and 70 °C, representing the typical temperature range of Niger Delta fluid systems.  Results indicate that lower temperatures significantly increase corrosion rates, leading to pronounced wall thinning and elevated stress concentrations. Conversely, higher temperatures reduce corrosion intensity by promoting the formation of protective corrosion films. However, localized stress elevations at higher temperatures were also observed, which may be attributed to combined thermal expansion effects and residual corrosion-induced weakening. This demonstrates a non-linear interaction between temperature, corrosion progression, and stress response. The study recommends maintaining sufficiently high fluid temperatures to mitigate corrosion. Further studies are needed to define the temperature range where corrosion behaves linearly, to support optimal design and operation while preventing conditions that could impair system performance and flow assurance. The result provides technical insight for the development of an integrity management strategy for optimum pipeline safety.
Parameter-Efficient Fine-Tuning for Sonar Shipwreck Segmentation: A Seed Averaged Study with SegFormer and LoRA Beruwalage, Shehan Maxwell; Yin, Chunyong; Raza, Muhammad; Kannangara, Deshan Sachintha; Hendavitharana, Sachini Amani
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.454

Abstract

Accurate segmentation of shipwreck targets in sonar imagery is important for underwater archaeology, marine monitoring, and search operations, but the task remains difficult because labeled sonar masks are scarce and full adaptation of transformer models can be computationally expensive. This study evaluates whether parameter-efficient fine-tuning can provide a practical alternative for binary sonar shipwreck segmentation. Using SegFormer-B0 initialized from a pretrained checkpoint, three adaptation strategies were compared under a consistent protocol: full fine-tuning of all model parameters (FullFT), training only the segmentation head (Head-only), and LoRA-based adaptation of selected linear layers together with head training (LoRA-A+Head). Models were selected by the best validation epoch and evaluated on a held-out test set. Across three random seeds, FullFT achieved the best performance, with a Dice score of 0.614 ± 0.008 and IoU of 0.487 ± 0.007. LoRA-A+Head achieved a Dice score of 0.546 ± 0.010 and IoU of 0.401 ± 0.008 while updating only 1.57% of the parameters, whereas Head-only reached 0.494 ± 0.010 Dice and 0.354 ± 0.008 IoU. These results show a clear accuracy efficiency trade off, full fine-tuning gives the highest accuracy, whereas LoRA-A+Head offers a practical option when reducing the number of updated parameters is important. The findings support the use of parameter-efficient adaptation for sonar segmentation in compute-limited settings.
Enhancing the Mechanical Properties of Concrete Using Natural Fiber Reinforcement: A Comparative Study of Bamboo, Banana, and Jute Fibers Md. Liton Rabbani; Md. Rashedul Islam; Md. Shahoriar Pulok; Rakibul Hasan; Md. Shaheen Al Mamun; Dhruboraj Roy; Rafaun Sultana Shiuly; Md. Atiqul Hasan
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.458

Abstract

With the construction industry seeking to mitigate its carbon footprint, utilizing agricultural by-products as reinforcement offers a promising pathway toward eco-friendly infrastructure. While previous studies have explored synthetic fibers, there remains a critical research gap in the comparative performance and statistical consistency of diverse natural fibers like banana, jute, and bamboo within a concrete matrix. This study investigates the mechanical properties of concrete mixes incorporating these fibers at varying volume fractions (0.5%–2.0%). The evaluation focuses on compressive, split tensile, and flexural strengths at 7-day and 28-day curing intervals. Key findings reveal that banana fiber at 0.5% achieved the highest absolute compressive and flexural strengths of 37.32 MPa and 6.12 MPa, respectively, after 28 days. However, performance for banana and jute fibers generally declined at higher dosages due to increased porosity. Conversely, bamboo fiber demonstrated superior reliability and consistency, reaching a peak split tensile strength of 5.02 MPa at 2.0% loading and maintaining steady growth across all proportions. This suggests that while banana fiber provides maximum load-bearing capacity at low volumes, bamboo fiber is preferable for applications requiring predictable mechanical scaling. These results provide foundational data for the implementation of natural fiber-reinforced concrete in sustainable structural applications, highlighting a viable strategy for reducing reliance on carbon-intensive materials while enhancing the energy absorption and ductility of cementitious composites.
Aerodynamic Optimization of Horizontal Axis Wind Turbine Blades Using Winglet and Spoiler Add-Ons: A CFD Study Alabi, Oluwaseyi Omotayo; Laoye, Adeoti Oyegbori; Salisu, Saidat Abisoye
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.459

Abstract

The aerodynamic performance of wind turbine blades plays a critical role in maximizing energy generation and overall system efficiency, making it a key consideration in modern renewable energy design. Despite extensive research on blade optimization, there remains a notable gap in understanding the combined effects of spoiler and winglet geometries, particularly their size and orientation on aerodynamic efficiency under low wind speed conditions. This study aims to address this gap by conducting a comprehensive numerical investigation into the influence of spoiler and winglet configurations on wind turbine performance. Computational Fluid Dynamics (CFD) simulations were performed using COMSOL Multiphysics, with the k-ε turbulence model employed to accurately capture turbulent flow behavior. A detailed parametric analysis was carried out, considering winglet height (4%–13% of blade radius), cant angle (20°–90°), twist angle (−2° to 12°), and tip speed ratio (0.02–1.12) at a wind velocity of 3 m/s. The results reveal that optimal combinations of spoiler and winglet parameters significantly enhance aerodynamic efficiency. The study identifies specific design ranges that maximize power output, achieving a peak aerodynamic power of 62.8 W. Although the addition of these aerodynamic devices increases the inertia of the turbine, the system performance improves, with an observed increase in output power of approximately 12%. These findings provide valuable insights for the design and optimization of wind turbine blades, particularly for low wind speed applications.
Nonlinear Prediction with Data Augmentation and Regularization: A Ensembled LSTM–XGBoost Model Devine Grace D. Funcion; Marvee Cheska B. Natividad
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.478

Abstract

Nonlinear prediction faces its main challenge in the form of overfitting, which leads to inaccurate predictions. The problem becomes evident when researchers attempt to apply advanced deep learning systems to small agricultural data collections. Researchers use knowledge discovery in databases (KDD) to evaluate regularization methods with ensemble techniques, but have not sufficiently explored how structured data augmentation interacts with MaxNorm regularization. The research explores how a sliding-window transformation, together with bootstrap augmentation methods, works when ElasticNet, Bayesian, and MaxNorm regularization techniques are combined into an LSTM-XGBoost prediction system to predict Tikog grass demand. The research showed that data augmentation techniques helped reduce model overfitting, thereby improving performance on prediction tasks. Among the regularization strategies applied to LSTM, MaxNorm achieved the largest reduction in error, with testing MSE decreasing from 0.060472 to 0.002090 after augmentation. A comparative evaluation further shows that LSTM-XGBoost achieved the highest overall performance (R² = 0.997806), while deep learning models showed greater sensitivity to augmentation and regularization strategies. These findings highlight that structured time-series augmentation combined with norm-based regularization enhances generalization capability, particularly for high-capacity sequence models trained on limited agricultural data.
AVNPR-Net: A Real-Time Deep Learning Framework for Robust Vehicle Number Plate Detection and Recognition Ahmad Ijaz; Tayyba Sarfraz; Tanzeela Bibi; Muhammad Usman
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.495

Abstract

AVNPR systems are critical in intelligent transportation, monitoring, and law enforcement systems. Nevertheless, the current systems are usually challenged by the issues of dissimilar illumination, obstruction, and the diversity of plate formats, which restrict their practical applicability. To solve these problems, this paper suggests a real-time deep learning-driven AVNPR framework that incorporates effective detection and recognition systems.  The proposed system employs the YOLOv8 object detector model to localize number plates with high accuracy and speed, as well as a lightweight recognition module to identify alphanumeric characters. A custom dataset with different types of vehicles in different environmental conditions was created and improved with the help of preprocessing and data augmentation methods to make the model more robust. In the experiments, the proposed system demonstrated an overall system accuracy of 98.7%, representing the combined number plate detection and character recognition results. The mAP@0.5 is 97%, and mAP 0.5-0.95 is 91%, as well as high precision, recall, and F1-score, which suggests that it shows potential applicability across varying conditions in the assessed dataset and suggests that it may be suitable for real-world applications. The system is also implemented with a Flask-based web application, and it supports image based and real-time webcam detection. The results indicate that the proposed framework provides a viable, efficient, and deployable solution to AVNPR applications. The work will lead to the creation of scalable and real-time intelligent transportation systems and give a basis for future advancement in the improvement of robust vehicle recognition in challenging conditions.
AI-Assisted Design and Control of Smart Electromechanical Devices for Energy-Efficient Applications Timileyin Opeyemi Akande; Osinachi Victor Chukwujama; Monsuru Olalekan Abdullahi; Princewill Kalio
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.408

Abstract

The growing global demand for sustainable energy has elevated the importance of energy-efficient electromechanical systems in applications such as electric vehicles (EVs), renewable energy conversion, and industrial automation. These systems reduce carbon emissions and operational costs by optimizing power use, aligning with UN Sustainable Development Goals and 2050 net-zero targets. However, traditional control methods like field-oriented control (FOC) and PID controllers struggle with nonlinear dynamics, parameter variations, and variable loads, resulting in suboptimal performance, higher energy losses, and reduced robustness. This paper addresses this gap by proposing an AI-assisted framework for designing and controlling smart electromechanical devices, using permanent magnet synchronous motor (PMSM) drives as a prototype. The approach integrates adaptive neural networks with reinforcement learning to enable real-time optimization of dynamic response, robustness, and energy efficiency. The system was rigorously simulated in MATLAB/Simulink using a d-q reference frame model under nominal, disturbed, and variable-load conditions. Results show significant improvements: transient settling time reduced by 42–58%, overshoot by 60–67%, and energy consumption by 12–18%, achieved through minimized torque ripple and losses. The framework also demonstrated superior disturbance rejection and parameter-variation stability. These advances position the proposed solution as a transformative approach for sustainable applications, enhancing efficiency in EV propulsion, renewable energy integration, and industrial automation, while paving the way for future hardware implementation and scalable AI-driven systems.
A Low-Cost Vision-based Fruit Sorting System for Robotic Applications Muhammad Afaq; Emanuele Lindo Secco
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.471

Abstract

Modern robotic systems address complex engineering challenges using artificial intelligence and machine learning techniques. In agricultural robotics, fruit identification and sorting remain challenging due to variations in size, shape, color, orientation, and lighting conditions. This study presents the design and implementation of a vision-based fruit sorting robotic system integrating YOLOv8-based object detection with robotic manipulation. A custom dataset consisting of images of 2 different fruits (namely banana and strawberry images), including single-fruit and multi-fruit scenarios, was used and manually annotated using bounding boxes in CVAT. The dataset was divided into training, validation, and test subsets to enable robust model development under realistic operational conditions. A lightweight YOLOv8 model was trained using CUDA acceleration and optimized for edge deployment by selecting YOLOv8n to balance inference speed and detection accuracy. The trained model was converted to ONNX format and deployed on a Raspberry Pi 5 for real-time inference using live camera input. Evaluation on an independent test dataset achieved a precision of 0.999, recall of 1.000, mAP@0.5 of 0.995, and mAP@0.5:0.95 of 0.963 under controlled experimental conditions with limited object classes. The modular architecture enables low-cost and scalable deployment and provides a foundation for future enhancements, including closed-loop robotic control, additional object categories, and operation in more dynamic environments.

Page 5 of 6 | Total Record : 54

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