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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 32 Documents
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The Role of Atomic-Scale Disorder in Tailoring the Functional Properties of Crystalline Materials: A Comprehensive Review Rahman, Md Sultanur; Uddin, Md Jasim; Hasan, Rakib; Mia, Md Mehedi Hasan
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.326

Abstract

It has long been believed that crystalline solids will always have atomic-scale disorder, which includes vacancies, interstitials, andesite defects, local strain fields, short-range compositional changes, and amorphous pockets. The functional qualities of materials can be controlled by redefining disorder as a flexible and adjustable design parameter. Across classes of crystalline materials (oxides, chalcogenides, perovskites, semiconductors, and two-dimensional crystals), we synthesize experimental and theoretical advances demonstrate how particular types and distributions of atomic-scale disorder alter charge-carrier dynamics, optical absorption and emission, magnetic ordering, ionic conductivity, thermal transport, and mechanical response. Mechanistic relationships are highlighted, including how correlated defect complexes and local strain mediate polaron generation and carrier mobility, how interface disorder and grain-boundary structure control ion transport and catalytic activity, and how point defects alter electronic band edges and trap states. From total-scattering PDF analysis and advanced spectroscopies to aberration-corrected TEM, atom probe tomography, and scanning probe microscopies, we go over characterization tools and how data-driven models, large-scale molecular dynamics, and first-principles calculations are coming together to predict and direct disorder engineering. Successful methods for improving device performance such as defect-enabled light emission, dopant-activated ionic conductors, and disorder-stabilized phases are highlighted in case studies. We conclude with useful recommendations for intentional disorder design and point out unresolved issues, such as in-operando characterization, multiscale modelling, and controlled defect synthesis, providing a roadmap for utilizing atomic-scale disorder to develop next-generation functional materials.
Ensemble Learning Framework for Image-Based Crop Disease Detection Using CNN Models Betrand, Chidi Ukamaka; Benson-Emenike, Mercy Eberechi; Kelechi, Douglas Allswell; Onukwugha, Chinwe Gilean; Oragba, Nneka Martina
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.330

Abstract

Crop diseases pose a significant threat to global food security, causing substantial yield losses estimated at 10-40% annually. Traditional methods of disease identification, reliant on visual inspection by farmers or experts, are often subjective, time-consuming, and limited by the availability of specialists. This study proposes an ensemble learning framework for robust image-based crop disease detection, specifically designed to address the challenges of heterogeneous, non-Independent and Identically Distributed (non-IID) agricultural datasets in decentralized environments. Utilizing the Plant Village dataset, we implement a stacking ensemble model integrating diverse Convolutional Neural Networks (CNNs) such as VGG (Visual Geometry Group), ResNet, and Inception as base learners, with a meta-learner to optimize prediction fusion. The system employs comprehensive data preprocessing, including resizing, normalization, noise removal, segmentation, and augmentation, to enhance robustness against real-world variability. Transfer learning with ResNet50 was adopted as a baseline model. The baseline ResNet50 achieved 59% test accuracy across seven grape and potato disease classes. The ensemble model improved performance, attaining 63% accuracy with average precision, recall, and F1-scores of 56%, 52%, and 52% respectively. Class imbalance remained a limiting factor for certain categories. The ensemble learning approach outperformed individual models, demonstrating improved generalization across diverse datasets. Although computational demands and imbalance challenges persist, the system provides a promising AI-driven pipeline for accurate crop disease diagnosis, supporting sustainable agricultural practices.
Hybrid Fuzzy–Multi-Objective Particle Swarm Optimization Control for Real-Time Energy Management in PV-Powered Fast Charging Infrastructure for Electric Vehicles Elgammal, Adel
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.339

Abstract

This paper proposes an innovative Fuzzy–Multi-Objective Particle Swarm Optimization (Fuzzy-MOPSO) based hybrid control strategy for real-time energy management in PV-integrated fast charging systems for EVs. The developed approach combines fuzzy logic control and multi-objective optimization algorithm to achieve dynamic balance between charge rate, power quality, grid stability, and cost of energy. This fuzzy controller can be flexibly used in the presence of variable and uncertainty conditions (e.g., fluctuated solar irradiance, changing EV charging request, grid voltage disturbance) since it has gradual control operations by adjusting converter duty ratios and charging current values. The MOPSO algorithm simultaneously optimizes the multiple antagonistic objectives such as minimization of THD, unity PF with less charging time and increased PV utilization efficiency by adjust fuzzy membership functions and rule weights in real-time. Simulation results in MATLAB/Simulink show that the hybrid controller performs better than classical PI controllers or single fuzzy or PSO based control system. The Fuzzy-MOPSO controller also limits the THD 0.995, and charging efficiency enhancement of (8–12%) with stochastic PV and load changes, in conformity to IEEE-519. Excessively generated energy cost are reduced as well by 15% through the optimal control on the power flow between PV generation, storage and grid. The hybridization of fuzzy reasoning and swarm-based optimization provides for fast transient response, renewable intermittency robustness, and grid integration sustainability. These findings validate that the proposed Fuzzy-MOPSO technique is an appropriate approach to intelligent, efficient and eco-friendly FCI of fast charging in REN smart cities.
Incremental Development of a Framework for Mitigating Adversarial Attacks on CNN Models Nisar, Maaz; Fayyaz, Nabeel; Ahmed, Muhammad Abdullah; Shams, Muhammad Usman; Fareed, Bushra
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.349

Abstract

This work explores the vulnerability of Convolutional Neural Networks (CNNs) to adversarial attacks, particularly focusing on the Fast Gradient Sign Method (FGSM). Adversarial attacks, which subtly manipulate input images to deceive machine learning models, pose significant threats to the security and reliability of CNN-based systems. The research introduces an enhanced methodology for identifying and mitigating these adversarial threats by incorporating an anti-noise predictor to separate adversarial noise and images, thereby improving detection accuracy. The proposed method was evaluated against multiple adversarial attack strategies using the MNIST dataset, demonstrating superior detection performance compared to existing techniques. Additionally, the study highlights the integration of Fourier domain-based noise accommodation, enhancing robustness against attacks. The findings contribute to the development of more resilient CNN models capable of effectively countering adversarial manipulations, emphasizing the importance of continuous adaptation and multi-layered defense strategies in securing machine learning systems.
A Qualitative and Literature-Based Technology Study of Drilling Rig Hoisting System Equipment Wardhani, Rachmasari Pramita; Simanjuntak, Cristo Nathanael Rayhan; Karim, Abdul Gafar
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.323

Abstract

Oil and gas energy in Indonesia plays a vital role in driving the country's economy, particularly as a foreign exchange earner and domestic energy supplier. The hoisting system is located on the surface of the rig and works in conjunction with other drilling systems. This study aims to provide an introduction and understanding of the equipment used in hoisting systems in oil and gas drilling activities, enabling students to understand the types of equipment and their uses. This relates to the drilling equipment course, which aims to provide students with a better understanding of the context of practical learning, which is difficult to conduct in the field for direct observation. Therefore, the research method used is a literature review with a qualitative approach to narrate and describe the process of activities carried out in practical learning, starting from the equipment introduction stage, gathering technical information from literature studies, and observing the drilling equipment using sketches and instructional videos of the equipment used in the drilling hoisting system. Based on these observations, students are able to explain and understand the scope of the hoisting system in oil and gas drilling.
Semi-Automatic Women Safety System Using Real-Time Facial Distress Detection with Mandatory User Confirmation and Emergency Alert Mechanism Tiwari, Virendra Kumar; Agrawal, Jitendra; Bajpai, Sanjay; Kanathey, Kavita
Scientific Journal of Engineering Research Vol. 1 No. 4 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i4.2025.333

Abstract

Women’s safety remains a critical global concern. Conventional panic applications and wearable devices require manual activation, which is often impossible when the victim is in shock, physically restrained, or under extreme stress. This paper proposes a semi-automatic women-safety mobile system that continuously monitors the user’s facial expressions using a lightweight Convolutional Neural Network (CNN). When a high probability of distress-related emotions (fear, anger, or sadness) is detected for three consecutive frames, the system instantly triggers strong haptic vibration and displays a large full-screen one-tap SOS confirmation button. Only if the user explicitly taps this button within 7 seconds does the system activate a loud deterrent siren and send the current GPS location along with a pre-recorded emergency message to pre-selected trusted contacts and, if the user has opted in during setup, to local emergency services. Experimental results on a combined dataset of approximately 50,000 facial images show a seven-class emotion classification accuracy of 89%. Real-world field trials conducted with 25 female volunteers in public environments recorded zero false or unintended emergency alerts, with an average time from first distress detection to confirmation screen appearance of 6.4 seconds and an average end-to-end alert transmission time of 6.4 seconds (including user confirmation). This is significantly faster than the 15–18 seconds required by traditional manual panic applications, while eliminating the risk of erroneous alerts that would occur in a fully automatic system. The proposed framework offers a practical, privacy-preserving, and ethically responsible solution that can be readily deployed on existing smartphones and wearable devices, contributing meaningfully to AI-driven personal safety technologies.
Integrated Vision-PLC Control Architecture for High-Performance Delta Robot Sorting in Industrial Automation Vo, Kim-Thanh; Nghia, Bui-Duc; Tran, Huy-Vu; Huynh, Thanh-Tuan; Nguyen, Huy-Bao; Nguyen, Phong-Luu; Nguyen, Van-Tuan; Phan, Anh-Quoc; Phung, Son-Thanh; Nguyen, Van-Dong-Hai; Nguyen, Binh-Hau; Nguyen, Van-Hiep; Nguyen, Thanh-Binh
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.337

Abstract

The rapid development of automation and robotics has increased the demand for high-performance industrial systems, in which Delta robots play a crucial role due to their lightweight structure, high speed, and precise positioning capability. This study aims to design, implement, and evaluate a Delta robot-based product classification system integrating PLC S7-1200 control and Machine vision. The proposed system employs a camera to detect object shape, color, and position on a conveyor, while a PC processes the image data and computes the robot’s inverse kinematics before transmitting control commands to the PLC. A hardware model of the Delta robot was designed and fabricated, and a dual-mode control application was developed to monitor and operate the robot in real time. Experimental results demonstrate that the system achieves stable operation, with a classification speed of up to 20 products per minute and an accuracy of approximately 95.7% for picking and placing tasks. The findings confirm the feasibility and effectiveness of integrating vision-based detection with high-speed parallel robot control for industrial sorting applications. The study also provides a foundation for further optimization in processing speed, mechanical design, and advanced image-processing techniques to enhance system performance in practical manufacturing environments.
Mesh Independence and Reynolds Number Sensitivity for External Automotive Aerodynamics Simulations Almaghrebi, Mohammed; Ali, Ahmed Atta Elhussein
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.353

Abstract

Aerodynamic prediction for full scale passenger vehicles relies on the use of mesh resolutions which accurately represent boundary layer evolution and wake dynamics while maintaining reasonable computational expense. To verify the drag prediction for two production-derived vehicle geometries (Notchback and Hatchback) simulated at 15° steady state crosswind using incompressible RANS with SST k−ω turbulence models, the verification process consisted of a systematic set of five progressively refined polyhedral meshes (1.5 million cells - 7.2 million cells) created using a controlled refinement template to maintain consistent near-wall treatment within all five meshes. The drag results showed significant improvement from the coarsest mesh to the finest mesh (≈ 14% improvement for Notchback ≈ 12% improvement for Hatchback) and then clearly exhibited asymptotic results as evidenced by the difference between M4 and M5 decreasing to less than approximately 1.5%, indicating that M4 provides mesh-independent accuracy with over 20% less computational cost than M5. Furthermore, a Reynolds number sweep across the range of representative full-scale Reynolds number values demonstrated that drag is effectively insensitive to Reynolds number once the fully turbulent regime is reached and wake structures between the Notchback and Hatchback. Through this analysis it has been determined that targeted refinement strategies around A-pillar and rear-end separation zones and the near wake will provide the greatest accuracy and cost-effective use of computational resources as compared to uniform global densification, thus providing a validated mesh resolution strategy for using RANS simulations to predict drag for full scale passenger cars under steady state conditions.
Hybrid Machine Learning Framework for Joint Prediction of Window Mean and Bit Error Rate in SC-LDPC Decoding Bibi, Tanzeela; Zhou, Hua; Akbar, Sana; Awasthi, Lalit
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.364

Abstract

Modern low-latency communication systems increasingly rely on spatially coupled low-density parity-check (SC-LDPC) codes combined with windowed decoding (WD) to achieve high reliability with reduced latency and memory requirements. However, evaluating the intrinsic trade-off between decoding complexity and error performance typically measured by the average window iteration count (WMEAN) and bit error rate (BER) still depends on computationally intensive Monte Carlo simulations, which limits rapid system optimization and real-time design exploration. To address this limitation, this paper proposes a hybrid machine learning framework for the joint, non-iterative prediction of WMEAN and BER using a single set of code and channel parameters. A high-fidelity dataset is generated through extensive SC-LDPC windowed decoding simulations across varying window sizes, coupling lengths, and signal-to-noise ratio (SNR) conditions. Based on this dataset, a multi-output Random Forest Regressor is trained to exploit the shared underlying decoding dynamics that govern both computational complexity and decoding reliability. The proposed model achieves accurate simultaneous prediction of WMEAN and BER, demonstrating strong generalization performance while significantly reducing system evaluation time compared to conventional simulation-based approaches. Feature-importance analysis further reveals the dominant influence of channel quality and coupling structure on both decoding effort and error performance. These results indicate that the proposed framework provides an effective surrogate modeling tool for fast design-space exploration and informed performance–complexity trade-off analysis. The methodology enables practical optimization of high-throughput SC-LDPC decoders and supports the development of adaptive and resource-efficient communication systems.
Design and Simulation of a Scalable IoT-Based Multi-Sensor Prototype for Pipeline Security Monitoring Udeh, Evander Chika; Agwu, Michael Chukwuebuka; Akinrinde, Pamilerin Samuel; Ugwuanyi, Nnaemeka Sunday; Nwogu, Akudo Ogechi
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.365

Abstract

Pipeline vandalism and leaks pose a significant threat to global energy infrastructure, leading to severe economic losses and environmental degradation. Traditional surveillance methods are often reactive and insufficient for monitoring vast, remote pipe-line networks in real-time. To address this gap, this study designs and simulates a multi-sensor Internet of Things (IoT) proto-type that integrates gas, vibration, and temperature monitoring for anomaly detection. The methodology employs a design-and-simulation approach using an Arduino Uno and ESP8266 Wi-Fi module within the Proteus environment. Key findings demonstrate the functional correctness of the system’s logic, achieving consistent alert triggering based on predefined heuristic thresholds with no failures in the simulated environment. These results imply that a low-cost, multi-modal sensor fusion approach provides a technically feasible foundation for future physical deployment in infrastructure security.

Page 3 of 4 | Total Record : 32

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