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Sinergi
Published by Universitas Mercu Buana
ISSN : 14102331     EISSN : 24601217     DOI : https://dx.doi.org/10.22441/sinergi
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high quality theoretical and empirical original research papers, review papers, and literature reviews that are closely related to the fields of Engineering (Mechanical, Electrical, Industrial, Civil, and Architecture). The theme of the paper is focused on new industrial applications and energy development that synergize with global, green and sustainable technologies. The journal registered in the CrossRef system with Digital Object Identifier (DOI). The journal has been indexed by Google Scholar, DOAJ, BASE, and EBSCO.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 27 Documents
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Search results for , issue "Vol 30, No 1 (2026)" : 27 Documents clear
Stress-strain behavior and residual strength of Petobo sand with variable fines content under consolidated undrained triaxial loading Vidayanti, Desiana; Rahardjo, Paulus Pramono; Nazir, Ramli
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.025

Abstract

Liquefaction was one of the primary causes of severe ground deformation during the 2018 Palu earthquake, particularly in the Petobo area, where large-scale flow liquefaction resulted in extensive ground displacement. Despite extensive field investigations, laboratory-based residual shear strength data for Petobo soil had not been available to explain the exceptional mobility of the flowslide. This study investigated the stress-strain response and post-liquefaction residual strength of Petobo silty sand with variable fines content using monotonic consolidated undrained (CU) triaxial tests. Reconstituted specimens were prepared using the moist tamping method with fines contents of approximately 9% and 26.4% and tested under three levels of initial mean effective stress. The results showed that specimens with higher fines content generated excess pore water pressure more rapidly and exhibited stronger contractive behavior at small strains. At large strains, however, both fines contents mobilized comparable residual strength levels, expressed as normalized residual strength ratios (qres/p’0) ranging from 0.79 to 0.94. These findings indicate that while fines content influenced early-stage contractive behavior and pore-pressure generation, its effect on post-liquefaction residual strength under very loose density conditions was limited. The residual shear resistance was primarily governed by the loose sand fabric and the effective stress level at the stabilized post-peak condition. This study provides the first laboratory-based residual shear strength data for Petobo silty sand, offering essential parameters for back-analysis, numerical modeling, and liquefaction hazard assessment related to flow-type deformation in Palu and similar alluvial environments. 
Utilization of teak wood powder waste as eco-friendly filler in HRS-WC asphalt: a comparative analysis of dry and wet Marshall mix methods Machsus, Machsus; Mawardi, Amalia Firdaus; Basuki, Rachmad; Hadi, Achmad Faiz; Islamey, Annizza Putri; Ndruru, Kornelius Sofinner; Wirawan, Arkaan Fadhiilah
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.024

Abstract

With the increasing demand for road durability driven by rapid economic development, innovative and sustainable approaches are essential to improve the strength and service life of road pavements. This study investigates the use of teak wood powder waste (TWPW) as a cost-effective and environmentally friendly filler material in Hot Rolled Sheet – Wearing Course (HRS-WC) asphalt mixtures. Utilizing bio-waste not only supports circular economic principles but also offers economic benefits by reducing reliance on conventional, more expensive fillers. The research evaluates various TWPW concentrations (0%, 0.3%, 0.6%, and 0.9%) and their effects on key Marshall test parameters, including stability, flow, Marshall Quotient (MQ), Voids in Mineral Aggregate (VMA), Voids in Mix (VIM), and Voids Filled with Asphalt (VFA). Samples were prepared using both dry and wet methods in accordance with Bina Marga (2018) specifications. The results indicate that the optimum filler content was 0.9% for the dry method (stability: 1042.68 kg) and 0.6% for the wet method (stability: 1161.14 kg). SEM analysis confirmed that filler dispersion significantly influences the internal structure and porosity of the mixture. At 0.3% and 0.6%, the filler was more evenly distributed, leading to improved compaction and mechanical performance. Conversely, agglomeration at 0.9% increased voids and reduced compaction quality. This study demonstrates that TWPW can serve as a viable low-cost filler alternative, maintaining pavement performance while reducing material costs and environmental impact. The findings support the adoption of sustainable waste utilization practices in road construction.
Sustainable competitiveness through community empowerment and the success factors of SMEs? Kartika, Hayu; Hardjomidjojo, Hartrisari; Machfud, Machfud; Warsiki, Endang
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.005

Abstract

This study aims to examine the relationship between sustainable competitiveness and community empowerment in the context of small and medium enterprises (SMEs) by conducting a systematic literature review using the PRISMA guidelines. The review identified relevant studies published between 2021 and 2025 in the Scopus database and analyzed them using the VOS Viewer bibliometrics tool. This study explores the role of empowerment in improving Sustainable Competitiveness. Findings show that the topics of sustainable competitiveness and community empowerment are still rarely discussed in an integrated manner, although both are significantly interrelated. The analysis identified six main dimensions that contribute to sustainable competitiveness in SMEs: Economic, Social, Environmental, Technological, Organizational, and Human Resources that correlate with sustainable competitiveness in SMEs. In various literature, sustainable competitiveness in SMEs is often discussed and has a close relationship with the use of technology in SMEs businesses. However, on the other hand, it also needs to be supported by the role of the community, which is included in the social dimension, which also plays an important role in SMEs that support business sustainability, foster cooperation networks, and improve the welfare of local communities. These findings provide a basis for insight and information to develop targeted business strategies and public policies aimed at enhancing sustainable competitiveness in SMEs through community engagement.
An effective and efficient vehicle detection using ER-EMA-YOLOv10n Kutika, Imanuel; Lahimade, Vicky Nolant Setyanto; Todingan, Tomi Heri Julius; Prasetya, Hebron; Sentinuwo, Steven Ray; Putro, Muhamad Dwisnanto
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.017

Abstract

Vehicle detection plays a key role in automating traffic analysis, a field that continues to advance rapidly. Vision-based systems identify vehicle types and sizes, but achieving high accuracy and efficiency remains a challenge. Reliable real-world deployment requires optimized models that balance performance and computational cost. YOLOv10n, the most efficient version of the YOLO family, offers a solid foundation for lightweight feature extraction. To improve its detection performance, this study proposes an enhanced version of YOLOv10n by incorporating a scale-aware attention mechanism. We proposed the Expanded Refinement Efficient Multi-Scale Attention (ER-EMA) module, which enhances feature encoding by capturing vehicle characteristics across multiple receptive fields. ER-EMA consists of two core components: the Expanded Converted Inverted Block (ECIB) and the Convolutional Refinement Block (CRB). These components use diverse convolutional kernels to extract and refine multi-frequency spatial features. Integrating ER-EMA into the YOLOv10n framework produces a more compact and accurate detection model. Experimental results show that the proposed model increases mAP@50 by 1%, while reducing the number of parameters by 0.1M and computation by 0.1 GFLOPS on the Vehicle-COCO dataset. On the UA-DETRAC benchmark, it achieves a 4% improvement in mAP@50:95, with a reduction of 0.2M in parameters and 0.4 GFLOPS in computational efficiency—outperforming the original YOLOv10n and prior methods in both performance and computational efficiency.
Enhanced Classification of Multi Abnormal Brain Tumors Detection Using Customized Inception V3 Arumalla, Nagaraju; Gampala, Veerraju
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.020

Abstract

A brain tumor (BT) is considered to be one of the most fatal diseases in the world, which also demands a very precise and early detection to be successfully addressed. The irregularities in the brain can be detected with the help of a magnetic resonance image, or MRI. Menigoma, glioma, pituitary tumours, and no-tumor are four categories of BT to be classified in this work according to an enhanced transfer learning (TL) approach, generated by the pretrained Inception V3 model. The preprocessing pipeline is new and includes data augmentation to reduce overfitting, a bilateral filter to remove noise, background cropping, and image scaling. The proposed method achieves training accuracy of 94.9% and validation accuracy of 93.8%. With a change in the hyperparameter (k-value), the validation and training accuracies improve to 95.3% and 96.8%, respectively. Furthermore, the model has a high level of generalization, where sensitivity is 92.8 percent, and specificity is 93.5 percent. The combination of transfer learning with the high-level enhancement and strengthening of pictures is novel. Nevertheless, among the factors that can affect generalizability, the variety and size of datasets are important. This model should be confirmed through further research using larger, more diverse datasets and explored in the context of clinical interpretability.
Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters Melinda, Melinda; Waladah, Buleun; Yunidar, Yunidar; Mahfuzha, Raudhatul; Gazali, Syahrul; Rusdiana, Siti; Basir, Nurlida
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.023

Abstract

This study investigates the effectiveness of two Finite Impulse Response (FIR) filter designs based on the Hanning and Blackman-Harris windows for preprocessing electroencephalography (EEG) signals collected from both neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). EEG signals were recorded using a 16-channel setup and band-pass filtered between 0.5 and 40 Hz to isolate relevant neural activity. Subsequently, the signals were processed independently using each FIR filter type. Performance evaluation was conducted using four quantitative metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Power Spectral Density (PSD). The Hanning window filter showed MAE values ranging from 0.079 to 0.325, MSE from 0.026 to 0.177, SNR between 7.56 and 15.86 dB, and PSD values from 5.3 to 9.08 × 10⁻³. These results demonstrate good noise attenuation while preserving signal morphology. In contrast, the Blackman-Harris window produced higher MAE (0.061–0.318) and MSE (0.019–0.172) but achieved significantly greater SNR improvements (7.77–17.4 dB) and tighter control over PSD (4.904 – 8.442 × 10⁻³), indicating superior noise suppression and reduced spectral leakage. A paired t-test confirmed that differences in all four performance metrics were statistically significant (p < 0.05) across both neurotypical and ASD subject groups. Despite the Hanning filter's computational simplicity, the Blackman-Harris filter demonstrated more robust performance, making it a more suitable choice for high-fidelity EEG signal analysis in clinical diagnostics and neuroscience research.  
Overestimation of load-resisting capacity in double-span welded steel beams: a comparative FEM study incorporating ductile damage and element deletion Mohd Zaman, Nor Idahyu; Subki, Nur Ezzaryn Asnawi; Hamid, Yazmin Sahol; Mansor, Hazrina
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.027

Abstract

The study investigates how different finite element modelling assumptions affect the predicted load-resisting behavior of welded beam-column connections in double-span steel beam systems subjected to column-removal scenarios. Existing numerical studies commonly neglect fracture and material degradation, which may result in unconservative estimates of structural capacity. To address this limitation, nonlinear static analyses were performed in ABAQUS using two simplified modelling approaches: (i) non-fracture models that exclude plasticity damage and element deletion, and (ii) fracture-based models that incorporate ductile damage criteria with element deletion. Structural responses were evaluated in terms of load-displacement relationships, moment-rotation behavior, and the development of tensile catenary action. The results indicate that accounting for plasticity damage and fracture significantly alters the predicted response, leading to markedly lower strength and deformation capacity compared to non-fracture models. In particular, the inclusion of fracture mechanisms resulted in an approximate 50% reduction in load-carrying capacity and catenary resistance. These findings demonstrate that neglecting fracture behavior can substantially overestimate the robustness of welded beam-column connections under extreme loading conditions. The study underscores the importance of structural performance in progressive collapse analyses. 

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