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Andi Adriansyah
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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 561 Documents
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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. 
Texture features-based automated classification for dental caries level images Yessi Jusman; Sartika Puspita; Nanang Kurniawan; Syahrul Gunawan; Berli Paripurna Kamiel; Zul Indra; Nor Ashidi Mat Isa
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.001

Abstract

Dental caries is a globally prevalent oral health issue posing substantial challenges regarding health outcomes and economic burden. Early detection is critical to prevent the progression of the disease and ensure effective treatment. This study aims to develop a machine learning-based system for classifying dental caries severity using X-ray radiographic images. The proposed system integrates two prominent feature extraction techniques: Histogram of Oriented Gradients (HOG) and Haar Wavelet Transform, applied at varying levels (HOG 50×50, HOG 70×70, Haar Level 1, and Haar Level 2) to capture both texture and frequency-based features. These extracted features are subsequently classified using two machine learning algorithms, Support Vector Machines (SVM) and k-Nearest Neighbors (KNN), across four models: Cubic SVM, Quadratic SVM, Weighted KNN, and Fine KNN. A dataset of 347 dental X-ray images was expanded to 1,388 through augmentation techniques and pre-processed into grayscale for consistency. The results unveiled that combining Haar Wavelet features with the KNN classifier yielded the highest classification accuracy, reaching 97.99% during training and an AUC of 0.99. These findings underscore the potential of combining advanced feature extraction methods with robust machine learning algorithms to enhance the precision of dental caries detection in clinical practice. This system presents a significant step forward in automating diagnostic procedures, providing a reliable and efficient tool for early caries detection, ultimately contributing to improved patient outcomes. 
Influence of water and oil quenching on the microstructure and mechanical properties of S45C steel Enok Mardiah; Suharmadi Suharmadi; Farrah Anis Fazliatul Adnan; Jong Soo Rhyee; Dianta Ginting
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.002

Abstract

S45C steel, commonly used in industrial applications due to its balanced mechanical properties, often requires further heat treatment to meet specific functional requirements. However, selecting the appropriate quenching medium, such as water or oil, significantly affects the steel's microstructural and mechanical outcomes, creating a trade-off between hardness and strength. This study systematically investigates the influence of water and oil quenching on the microstructure, hardness, and tensile properties of S45C steel. Specimens were austenitized at 900°C, held for 45 minutes, and rapidly quenched in either water or oil. Mechanical tests included hardness measurement using the Rockwell C (HRC) scale, ultimate tensile load, and ultimate tensile strength testing conducted on a universal testing machine. The Hall-Petch theory was applied to analyze the relationship between grain size and hardness. Results demonstrate significant improvements in mechanical properties with both quenching methods. Water quenching achieved the maximum hardness (55.7 HRC) compared to untreated steel (25.6 HRC), representing a 118% enhancement, with an ultimate tensile strength of 891.4 MPa versus 632.3 MPa for the baseline (41% improvement). Oil quenching demonstrated a moderate increase in hardness to 42.9 HRC (68% enhancement) while achieving a superior ultimate tensile strength of 1041.3 MPa (65% improvement). These findings establish critical trade-offs in quenching media selection: water quenching maximizes hardness for wear-resistant applications, while oil quenching optimizes tensile strength for structural components requiring superior load-bearing capacity. 
Nonparametric Statistical Approach For Failure Prioritisation in Reliability Centered Maintenance at Petrochemical Laboratory Maria Ulfah; Ratna Ekawati; Dicka Prameswara
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.003

Abstract

The petrochemical company, a major producer of PTA in Indonesia, has an important laboratory to ensure product quality, with capillary electrophoresis as one of the vital instruments for determining quality. The maintenance strategy currently implemented, namely preventive and corrective maintenance, is not optimal in preventing sudden downtime. The Reliability Centered Maintenance (RCM) method is proposed as a more systematic approach. In the RCM process, Failure Mode and Effect Analysis (FMEA) is used to identify failure risks; however, the conventional FMEA method has limitations in determining the Risk Priority Number (RPN), which can result in identical values. This study uses the Reliability Centered Maintenance (RCM) method, which includes qualitative and quantitative analyses. One of the qualitative analyses is determining FMEA, with the output being the RPN value. The qualitative analysis of the RCM method resulted in identifying critical instruments and critical instrument capability limits, creating a critical instrument system block diagram, and identifying FMEA, which produced 45 critical instrument failure modes (FM). The quantitative study proposed a nonparametric statistical approach, namely the Mann-Whitney and Kruskal-Wallis tests, to optimize failure priority ranking. The Mann-Whitney test results for FM with two identical RPN values (Uvalue>37;p>0.05) showed insignificant results and through expert consideration was able to distinguish priorities among 16 FM with identical RPN, while the Kruskal-Wallis test results for FM with more than two identical RPN values (Hvalue<5.99;p>0.05) showed insignificant results and through expert consideration was able to distinguish priorities in 13 FM with three or more identical RPN values.
Influence of lemon skin powder on the mechanical and thermal properties of polyvinyl alcohol (PVA)/cassava starch biocomposites prepared by solution casting method Salahuddin Junus; Revvan Rifada Pradiza; Muhammad Yusuf; Mahros Darsin; Gaguk Jatisukamto; Mochamad Asrofi
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.004

Abstract

More environmentally friendly alternative materials are needed to replace synthetic plastics to prevent environmental pollution. One biocomposite, a mixture of polyvinyl alcohol (PVA) and cassava starch, is an appropriate solution; however, its mechanical properties are low. Lemon skin, which is often considered waste, contains promising cellulose and phenolic extract content, making it a potential superior and functional filler for biocomposites, particularly in active packaging applications. This study presents an investigation into the use of lemon skin powder (LSP) as a filler for PVA- and cassava starch-based biocomposites. The biocomposites were prepared using the solvent casting method by varying the concentration of lemon skin powder filler, namely 0%, 1%, 3%, and 5%, with the final product being a film.  Analysis was carried out on the mechanical properties, morphology (Scanning Electron Microscopy (SEM)), and thermal properties of the film. The results show that the mechanical properties of the film increased when LSP was added, with the highest tensile strength of 13.82 MPa for biocomposite films containing 5% LSP compared to the tensile strength of pure PVA and PVA/cassava starch blends. In addition, the thermal properties of biocomposites also increased at this content, as evidenced by an increase in the initial decomposition temperature. Specifically, the initial decomposition temperature of pure PVA, which was 204°C, increased to 214°C in biocomposites with 5% LSP. This improvement in thermal properties and tensile strength can be attributed to the strong interfacial bonding between the filler and the matrix, which contributes to the overall compact structure of the biocomposite. Morphological observations confirm the increased interface interaction and uniformity of filler distribution in the composite. These results confirm that lemon skin fibers have positive potential as reinforcements for PVA and cassava starch-based biocomposites, particularly in food packaging applications.
Coating materials to enhance the corrosion resistance of magnesium-based implants: a review Slamet Saefudin; Purnomo Purnomo; Muhammad Omar Rusydi; Samsudi Raharjo; Kuzmin Anton; M. Edi Pujianto
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.005

Abstract

Biodegradable magnesium implants have attracted significant attention in orthopedics due to their low density, biocompatibility, and natural degradability in the human body. However, their clinical application has been limited by an excessively rapid degradation rate, which may compromise mechanical stability and disrupt tissue healing. To address this challenge, surface coating has been explored as an effective strategy to control the degradation rate, improve corrosion resistance, and preserve the mechanical integrity of magnesium implants. This review analyzed and compared various coating materials and methods applied to magnesium-based implants, including polymers, ceramics, metals, and composites. Each material category was found to offer distinct advantages and limitations in terms of biocompatibility, corrosion protection, and mechanical reinforcement. Furthermore, the study highlighted that the choice of coating method-such as dip coating, physical vapor deposition, micro-arc oxidation, or electrodeposition-significantly affected the performance of the protective layer. A structured literature search and qualitative synthesis of recent studies published between 2020 and 2025 were conducted to assess coating performance, identify trends, and evaluate challenges in clinical translation. The findings indicated that composite and multilayer coatings provided the most promising balance of corrosion resistance, bioactivity, and mechanical strength, despite fabrication complexity. This review concluded that the development of multifunctional coatings and standardized testing protocols remains crucial for advancing the clinical application of magnesium-based biodegradable implants.  
Integrating industrial engineering tools and behavioral modeling for optimizing operational efficiency in nature-based tourism services Didit Damur Rochman; Louie A. Divinagracia; Andhi Sukma
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.006

Abstract

This study proposes a hybrid approach integrating behavioral modeling (PLS-SEM) and engineering diagnostics (Value Stream Mapping, Time Study, and Spaghetti Diagram) to evaluate and optimize service performance in nature-based tourism. Using survey data from 280 visitors to two West Java destinations, we test the effects of ergonomic design, service quality, technology integration, and environmental perception on operational efficiency, tourist satisfaction, and revisit intention. The structural model indicates that ergonomic design and environmental perception significantly enhance operational efficiency (ERG→OPE β = 0.404; ENV→OPE β = 0.552), which in turn strongly predicts satisfaction (OPE→SAT β = 0.944) and revisit intention (OPE→RI β = 0.619). The model shows substantial explanatory power (R²: OPE = 0.621; SAT = 0.891; RI = 0.383). Field diagnostics corroborate these findings: non-value-added time accounts for 38% of the end-to-end process, with notable delays at ticketing (+2.3 minutes vs standard) and route overlaps in high-density zones. Results suggest that environmental and ergonomic factors outperform technology and formal service attributes in driving outcomes within nature-based contexts. Theoretically, the study extends the S-O-R framework by positioning operational efficiency as a meso-level mediator linking physical stimuli to behavioral responses and bridging perception-based modeling with systems diagnostics. It provides actionable guidance for lean service redesign, wayfinding, and spatial reconfiguration to improve operational performance and visitor experience. 

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