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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 7 Documents
 1 
Search results for , issue "Vol. 2 No. 1 (2026): March Article in Process" : 7 Documents clear
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.
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.
A Novel Wavelet-Based Approach for Transmission Line Fault Detection and Protection Emon, Asif Eakball; Ahammad, Jalal
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.v2i1.2026.374

Abstract

The reliable operation of modern power systems is critically dependent on the rapid and accurate isolation of transmission line faults, as failures can trigger cascading outages with severe socioeconomic consequences. While conventional protection schemes like overcurrent and distance relays are widely deployed, they exhibit limitations in speed, selectivity, and performance under high-impedance or evolving fault conditions, representing a significant gap in ensuring grid resilience. To address this, the objective of this research is to design and validate a novel Wavelet Transform Analysis with traditional relaying to enhance fault detection and classification. Through comprehensive modeling and simulation in MATLAB/Simulink, the proposed system demonstrated a mean fault detection time of 11.4 milliseconds and an accuracy of 99.8%, significantly outperforming conventional methods, particularly in challenging scenarios such as high-impedance and intermittent faults. These findings imply that the wavelet-enhanced framework offers a robust, adaptive solution for modern and future power networks, contributing directly to improved system stability, reduced outage times, and a foundational step toward intelligent, self-securing grid infrastructure.
Interpretable Deep Learning for Type 2 Diabetes Risk Prediction in Women Following Gestational Diabetes Prashanthan, Amirthanathan; Prashanthan, Jenifar
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.v2i1.2026.376

Abstract

Women with gestational diabetes mellitus (GDM) face a 7-10 times elevated risk of developing Type 2 Diabetes Mellitus (T2DM), yet current predictive models demonstrate limited accuracy (AUC-ROC: 0.70-0.85) and insufficient interpretability for clinical adoption. This study addresses the critical need for accurate, transparent risk prediction tools by developing an interpretable deep learning framework integrating bidirectional long short-term memory (BiLSTM) networks with attention mechanisms and SHapley Additive exPlanations (SHAP). Using a synthetic dataset of 6,000 simulated post-GDM women with 28 clinical risk factors, the BiLSTM-Attention model was evaluated through stratified 10-fold cross-validation against five baseline models. The proposed model achieved exceptional performance with 98.45% accuracy, 98.80% precision, 98.30% recall, 98.55% F1-score, 96.85% MCC, and 0.9968 AUC-ROC, significantly outperforming all baselines (p < 0.05). SHAP analysis identified recurrent GDM history, elevated HbA1c, and impaired glucose tolerance as primary predictors, while highlighting modifiable factors including physical inactivity, dietary habits, and obesity as actionable intervention targets. This proof-of-concept demonstrates the methodological feasibility of combining high-performance deep learning with explainable AI for T2DM risk stratification. However, synthetic data represents a significant limitation; comprehensive real-world clinical validation across diverse populations is essential before clinical implementation. The publicly available computational framework enables future validation studies to advance this approach toward clinical utility.

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