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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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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.

Page 4 of 4 | Total Record : 32

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