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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 59 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
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
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
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
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.
A Cost-Effective QR Code-Based Equipment Management System for Small-Scale Clinical Facilities Phong-Luu Nguyen; Dinh-Hai Vu; Trong-Bang Tran
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.430

Abstract

The rapid proliferation of medical devices in clinical settings necessitates efficient tracking and maintenance to ensure healthcare quality and cost optimization. (Gap) Despite technological advancements, many small to medium-sized clinics continue to rely on manual, paper-based equipment management systems. These traditional methods are prone to human error, lack real-time monitoring, and suffer from inefficient audit trails. (Objective) This study aims to develop a cost-effective, QR code-based equipment management system tailored specifically for small-scale clinical facilities. The proposed system integrates a Flutter-based cross-platform application with a centralized PostgreSQL database, utilizing standard webcams for QR code scanning to eliminate the need for expensive, dedicated scanning hard-ware. (Findings) Experimental implementations demonstrate that the system achieves a >95% QR code identification success rate at optimal scanning distances (0.3–1.0m) under standard lighting. Further-more, the architecture guarantees 99.2% network uptime, seamless real-time data synchronization, and supports up to 20 concurrent users with low database query latency (15–30 ms). Cost analysis indicates significant economic advantages, with first-year operational costs ranging from $300 to $600, markedly lower than commercial alternatives. (Implications) By replacing outdated manual methods with an auto-mated, role-based tracking system, this solution provides clinics with a robust, accessible, and scalable tool to enhance operational efficiency and streamline equipment lifecycle management.
Effect of Ni-Cr on the Mechanical Properties, Machinability, Microstructure and Corrosion Behaviour of Al5Si3Cu Alloy Omogbolade L. Adepitan; Olusegun Olufemi Ajide
Scientific Journal of Engineering Research Vol. 2 No. 4 (2026): December (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i4.2026.470

Abstract

This study was designed to explore the effects of varying amounts of Nickel (Ni) and Chromium (Cr) on the mechanical, microstructural, corrosion, and machining properties of Al5Si3Cu alloy. Samples were prepared using a sand-casting method with Ni-Cr additions ranging from 2g to 10g, and analysed using spectroscopy, mechanical testing (hardness, tensile, impact, and compression), machinability evaluation, and wear testing via pin-on-disc. The evolved microstructure was observed using X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM), while corrosion resistance was investigated using potentiodynamic polarisation. The results revealed that Ni-Cr additions led to a complex balance of effects. While corrosion resistance improved at 8g Ni-Cr, mechanical strength and machinability declined due to the formation of brittle intermetallic compounds. Wear resistance was highest at 2g Ni-Cr but deteriorated with higher additions. At 10g Ni-Cr, corrosion resistance and overall performance declined due to oversaturation and microstructural defects. It was concluded that varying percentage of Ni-Cr has different effect on these properties of the ternary alloy. However, excessive alloying resulted in embrittlement and reduced performance. The findings emphasise the importance of precisely controlling Ni-Cr content in aluminium alloys to achieve a desirable balance of properties. Future research should focus on composition optimisation and understanding intermetallic behaviour.
Neural Differential Cryptanalysis of GIFT-128 and ASCON via Deep Learning Muhammad Ahmad; Hua Zhou; Muhammad Usman; Tanzeela Bibi; Haider Ali; Maryum Shahzadi; Farah Javed
Scientific Journal of Engineering Research Vol. 2 No. 4 (2026): December (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i4.2026.491

Abstract

Differential analysis is a pivotal method for assessing the security of block ciphers; it distinguishes a cipher from a random permutation by tracing the propagation of plaintext differences. Traditional analytical methods face limitations when applied to complex algorithms, whereas the feature extraction capabilities of deep learning have opened up new avenues for cryptanalysis. To facilitate the security assessment of block ciphers, this paper proposes a novel construction method for a neural differential distinguisher that integrates traditional differential analysis with deep learning techniques. Regarding dataset construction, a multi-ciphertext-pair triplet input format is adopted to preserve differential features while capturing correlations across ciphertext pairs. The network architecture is based on Convolutional Neural Networks (CNNs) and incorporates a Residual Shrinkage Network to construct a deep dilated structure and a multi-scale feature fusion mechanism. Experimental results on the GIFT-128 and ASCON-PERMUTATION lightweight permutation-based cryptographic algorithm demonstrate the efficacy of this approach: for GIFT-128, the 6-round distinguisher reached a maximum accuracy of 99.70%, and the 7-round distinguisher reached 95.47% when using 32 ciphertext pairs; for the 4-round analysis of ASCON, the accuracy rate reached a maximum of 53.54%. These results validate the effectiveness of deep learning methods in the analysis of cryptographic security.
Kinetics and Statistical Analysis in Biogas Production: Optimization of Clostridium welchii Bioaugmentation of Solid Organic Waste from Shinko Yusufu Luka; Abdulhalim Musa Abubakar; Hassan Ahmed Saddiq; David Ufedo Apeh
Scientific Journal of Engineering Research Vol. 2 No. 4 (2026): December (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i4.2026.528

Abstract

Natural microbial communities in cassava peel (CP), sugarcane waste (SW), and poultry manure (PM) are often inadequate to sustain stable anaerobic digestion (AD) for efficient biogas production. This study employed bioaugmentation with Clostridium welchii to improve process stability and biogas production, thereby promoting the valorization of organic wastes generated in Shinko, Adamawa State. CP, SW, and PM were codigested at a 5:4:1 ratio under three conditions: a non-bioaugmented control (X) and two bioaugmented setups (Y and Z) containing 200 and 300 mL of Clostridium welchii, respectively. Biogas production over a 40-day digestion period was fitted to the Modified Gompertz, Cone, Fitzhugh, and Logistic kinetic models to estimate kinetic parameters. Model performance across the three bioreactor setups was evaluated using 29 statistical metrics. The maximum experimental biogas yields at day 40 were 1.19, 2.13, and 1.85 m3/kg for X, Y, and Z, respectively, with predicted values showing close agreement. Among all models, the Modified Gompertz model for setup Y demonstrated the best overall performance, combining the highest biogas yield, the fastest production rate (k), favorable shape factor (n), a short lag phase, and excellent statistical agreement (R2 > 0.9992). Bioaugmentation with 200 mL of Clostridium welchii proved the most efficient and stable strategy for codigesting CP, SW, and PM based on all 29 statistical criteria. Overall model performance ranked as Modified Gompertz > Cone > Logistic > Fitzhugh. Moderate bioaugmentation optimized microbial activity, shortened the lag phase, increased biogas production rates, and strengthened the predictive accuracy of the kinetic models.
ANFIS-Based PD-Fuzzy Control for Pendubot Stabilization Van-Long Mach; Dang-Khoa Huynh; Khanh-Hung Le; Vi-Khang Nguyen; Van-Hai-Dang Ma; Viet-Phi Le; Nguyen-Duy-Phuong Huynh; Le-Nhat Nguyen; Quang-Hoa Le; Van-Dong-Hai Nguyen
Scientific Journal of Engineering Research Vol. 2 No. 4 (2026): December (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i4.2026.540

Abstract

The Pendubot is a nonlinear underactuated system with two rotational degrees of freedom and a single actuator, making stabilization a challenging problem for intelligent and nonlinear control methods. Reliable balancing control is important for evaluating control strategies under both simulation and practical hardware constraints. However, although fuzzy and intelligent control methods have been investigated for Pendubot systems, the practical implementation and experimental behavior of ANFIS-based PD-Fuzzy control remain insufficiently documented, particularly in comparison with a conventional PD controller under the same laboratory conditions. This study aims to develop and evaluate an ANFIS-based PD-Fuzzy controller for TOP-position stabilization of a Pendubot. The nonlinear dynamics are formulated using the Euler-Lagrange method, while controllability of the linearized model is examined at the TOP and MID equilibrium points. The proposed controller uses ANFIS-based fuzzy blocks to approximate the proportional control actions, while derivative paths remain explicitly implemented. MATLAB/Simulink simulations show that the baseline PD and PD-Fuzzy controllers produce closely matched TOP-balancing responses, with settling times of approximately 2.36 and 2.37 s for link 1, respectively. Experimental evaluation using an STM32F407-based platform demonstrates practical TOP balancing; however, the baseline PD controller provides more favorable behavior than the PD-Fuzzy realization under the reported hardware conditions. These findings indicate that ANFIS-based PD-Fuzzy control is feasible for Pendubot stabilization but does not necessarily provide performance improvement over a conventional PD controller. The study therefore highlights the importance of hardware-aware tuning, sensor quality, and broader ANFIS training data for future improvements.

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