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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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BReMS-Net: Prediction-Guided Coarse-to-Fine Refinement with Boundary-Aware Multi-Scale Dilated Fusion for Robust Breast Mass Segmentation Tayyba Sarfraz; Tan Ling; Ahmad Ijaz
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.v2i3.2026.489

Abstract

Breast masses in mammograms are important to segment for computer-aided diagnosis (CAD) to enhance early detection and treatment decisions. Current approaches face challenges in segmenting lesions with low lesion-to-tissue contrast and diverse textures, resulting in misclassification or poor segmentation accuracy. To overcome this challenge, this paper introduces BReMS-Net, a multi-stage segmentation network to improve contextual learning and refined boundaries. We used an MBA-Net backbone with two major components: a Multi-scale Hybrid Dilated Convolution (MHD) module to extract multi-scale contextual features, and a Boundary Feature Auxiliary (BFA) module to strengthen boundary representations via coarse-to-fine feature fusion. Furthermore, a lightweight Prediction-Guided Refinement Module (PRM) uses initial predictions to produce attention maps, remove background clutter, and progressively refine boundary areas. The model has been evaluated on a cross-dataset basis, trained on the CBIS-DDSM dataset and tested on the INbreast dataset, and the results show that the BReMS-Net produces a Dice coefficient of 93.12% and an HD95 of 0.9826, which demonstrate competitive performance compared to several state-of-the-art deep learning methods. These results underline its generalization and robustness. Overall, the framework provides a robust and efficient approach to breast mass segmentation and has important implications for the performance and clinical relevance of automatic breast cancer diagnosis systems.
NRCC-LC: Noise-Robust Crowd Counting with Dynamic Label Correction under Noisy Supervision Abubakar Abdinur Hersi; Miaogen Ling; Muhammad Raza; Abdirahman Mohamed Hassan; Idris Aweis Hussien
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.v2i3.2026.494

Abstract

Crowd counting remains a challenge within computer vision due to many factors that affect the performance of available methods such as occlusion, scale variability, and perspective distortion. Additionally, many labels associated with crowd counting systems have high levels of noise caused by various real-world conditions. Although crowd counting methodologies have improved accuracy over recent years, the majority of crowd counting models still rely on clean real-time supervision and lack systems that can correct for dynamically corrupted labels, resulting in low robustness for crowd counting models when deployed in real-world applications. In this work we present a Noise-Robust Crowd Counting with Label Correction (NRCC-LC) framework to obtain reliable density estimates from noisy supervision. To accomplish this, our approach uses a combined CNN-Transformer architecture to capture both locally- and globally-relevant visual information (i.e., image content and context), along with a Noise-Robust Module (NRM) and a Dynamic Label Correction (DLC) mechanism. Our principle experimental results evaluated across four benchmark datasets: ShanghaiTech Part A, ShanghaiTech Part B, NWPU-Crowd, and JHU-Crowd++, indicate that the NRCC-LC exhibits competitive performance with respect to existing state-of-the-art crowd-counting methods; most notably, producing per-image MAEs of 97.8 and 392.3 on NWPU-Crowd. These experimental results additionally have real-world implications for improving public safety and urban planning; thus, through our novel method of noise-aware feature learning combined with iterative label correction, we can establish the potential of automated monitoring systems in complex, real-world environments to be significantly more reliable.
Deep Learning–Driven Anomaly Detection for IoT-Enabled Smart Engineering Systems Godfrey Perfectson Oise; Kevin Chinedu Pius; Felix Oshiorenoya Uloko; Immunhierokene Clinton Obrorindo; Roli Lydia Oshasha
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.v2i3.2026.432

Abstract

The rapid adoption of Internet of Things (IoT) technologies in smart engineering systems has increased the need for reliable anomaly detection mechanisms capable of identifying cyberattacks, operational faults, and abnormal system behaviors in complex cyber–physical environments. Existing rule-based and conventional machine learning approaches often struggle to effectively model the non-linear, high-dimensional, and highly imbalanced nature of IoT-generated multivariate time-series data, thereby limiting their capability to detect subtle and previously unseen anomalies. To address these challenges, this study proposes a deep learning–driven anomaly detection framework based on a hybrid CNN–LSTM autoencoder architecture for modeling spatiotemporal system behavior in IoT-enabled engineering environments. The proposed framework integrates convolutional neural networks for spatial feature extraction with long short-term memory networks for temporal dependency learning, while anomaly detection is performed using reconstruction error analysis and adaptive thresholding under unsupervised learning conditions. Experimental evaluation was conducted using the BATADAL-A dataset, which represents a realistic cyber–physical water distribution system. The results demonstrate stable convergence and strong generalization performance, with closely aligned training and validation losses throughout the learning process. The proposed framework achieved 90% overall accuracy, anomaly precision of 0.83, anomaly recall of 0.22, and an AUC of 0.677, indicating effective modeling of normal operational behavior but limited sensitivity to rare anomalous events. These findings demonstrate that the proposed CNN–LSTM autoencoder provides reliable low–false alarm monitoring for IoT-enabled smart engineering systems while highlighting the need for future improvements to enhance anomaly sensitivity and robustness in safety-critical applications.
A Study of Loss Weight Balance in Lightweight Self-Distilled Crowd Counting Muhammad Raza; Atta Ur Rahman; Pandula Pallewatta; Inayat Ur Rahman; Sahib Bahadar
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.v2i3.2026.493

Abstract

Lightweight crowd counting is important for real-time surveillance and resource-constrained deployment, where both computational efficiency and effective supervision are required. Although teacher-free self-distillation can improve lightweight density-regression models by guiding intermediate representations without an external teacher, the influence of composite loss weights in such frameworks has not been sufficiently analyzed. This paper presents a focused coefficient-wise loss-weight analysis within the Lightweight Self-Knowledge Distillation framework for single-image crowd counting. Instead of proposing a new architecture, the study investigates how the coefficients α, β, γ, and λ₂ affect optimization behavior and counting accuracy under a fixed experimental setup on ShanghaiTech Part B. Specifically, α controls intermediate feature alignment, β controls consistency supervision, γ controls direct density-regression supervision, and λ₂ controls the structural similarity term in the regression loss. The results show that moderate values of α and β improve performance by providing useful internal regularization, while excessive auxiliary weighting can slightly degrade accuracy. The analysis also indicates that γ should remain dominant because direct density-map regression is the primary learning signal. The best observed configuration is α = 6.0, β = 2.0, γ = 13.0, and λ₂ = 0.2, achieving 8.94 MAE and 11.51 RMSE on ShanghaiTech Part B. These findings highlight the importance of balanced supervision design within the evaluated LSKD framework on ShanghaiTech Part B.

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