cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andi Adriansyah
Contact Email
andi@mercubuana.ac.id
Phone
+628111884220
Journal Mail Official
sinergi@mercubuana.ac.id
Editorial Address
Fakultas Teknik Universitas Mercu Buana Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650 Tlp./Fax: +62215871335
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
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 26 Documents
 1   2   3 
Search results for , issue "vol. 30 no. 2 (2026)" : 26 Documents clear
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.
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. 
Extrusion parameter optimization to improve the mechanical performance of recycled PLA-PETG blends using Taguchi-GRA approach Annisa Fatimatus Zahro; Ihwan Ghazali; Achmad Pratama Rifai; Wangi Pandan Sari
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.007

Abstract

Recycled polymers offer a promising pathway toward sustainable additive manufacturing, but their mechanical performance is often limited by degradation and incompatibility between mixed materials. This study investigates the optimization of extrusion parameters for recycled PLA–PETG blends to improve tensile strength and ductility. The Taguchi method was applied to efficiently evaluate the effects of PLA content, temperature, and extrusion speed, while Grey Relational Analysis (GRA) was incorporated to enable simultaneous optimization of tensile strength and elongation, which cannot be achieved using Taguchi alone. The Taguchi–GRA approach identified 30 wt% PLA, 225 °C, and 300 mm/min as the optimal parameter set, producing filament with a tensile strength of 7.47 MPa and elongation of 1.58%, corresponding to a ~28% improvement over the lowest-performing condition. Although the optimized recycled filament exhibited significantly lower properties than commercial PETG, the performance is adequate for low-stress functional applications such as prototyping components and non-load-bearing parts. Fractographic analysis further revealed poor interlayer and interfacial adhesion, explaining the limited ductility of the recycled blends. These findings demonstrate that combining Taguchi and GRA provides a practical route for balancing strength and ductility in sustainable filament development and provides a foundation for future improvements through the use of compatibilizers, post-processing, and enhanced extrusion control.
4×1 Quarter-Circle Notched array antenna with independent feeding for enhanced beam steering mechanism at C-band frequency Tri Nur Arifin; Ganjar Febriyani Pratiwi; Erfiana Wahyuningsih; Arief Budi Santiko; Suisbiyanto Prasetya; Dian Rusdiyanto; Syah Alam; Yohanes Galih Adhiyoga
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.008

Abstract

One of the main challenges in microstrip antenna design is achieving adaptive beam steering. Such mechanism cannot be realized using an antenna array with a corporate feeding technique; instead, each antenna element must be fed independently with specific phase differences to steer the beam as needed. This study aimed to develop a microstrip antenna that can adjust its beam direction by applying an independent feeding technique to a 4×1 microstrip array. The theoretical development focused on calculating the required progressive phase shifts for each antenna element to achieve target steering angles. The proposed beam steering mechanism was implemented using a power splitter and 6-bit digital phase shifters connected to each element of the novel array design. This configuration successfully achieved a beam steering range of approximately ±36°, resulting in a total coverage of about 72°. The simulation results showed that the 4×1 array antenna achieved a gain of 10.11 dBi. Fabrication and measurement of the single element antenna with Quarter-Circle Notched (QCN) elements worked at frequency of 3.47 – 3.57 GHz with a bandwidth of 100 MHz. The measured 4×1 QCN array demonstrated performance within 3.32 – 3.75 GHz, achieving a better bandwidth of 430 MHz. The results also demonstrated that optimal gain could be achieved by adjusting the element spacing. Furthermore, variations in phase shift had been shown to enhance the antenna's beam scanning capability beyond theoretical expectations.
A traceability model for rental equipment in support companies within the oil and gas industry Asep Endih Nurhidayat; Rina Fitriana; Didien Suhardini; Asri Nugrahanti; Sannia Mareta
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.009

Abstract

Inefficiencies in equipment rental management within the oil and gas sector often lead to low transparency, slow data flow, and operational inaccuracies. This study aims to design and simulate a traceability model integrating Blockchain, the Internet of Things (IoT), and Quick Response (QR) Code technologies to improve data integrity and process efficiency. Using a mixed-method approach with object-oriented analysis and design under the Business Process Model and Notation (BPMN) framework, the system was developed and simulated through Bizagi Modeler. The proposed model consists of three components: the Tool Flow Sub-Model for equipment lifecycle tracking, the Document Flow Sub-Model for secure digital record management through blockchain, and the Grand Design Model that integrates processes and stakeholders into a unified traceability framework. Simulation results indicated a 91.7% improvement in process efficiency, reflecting significant reductions in transaction time and human error compared to the manual system. The integration of Blockchain, IoT, and QR Code technologies offers a novel approach to enhancing traceability and transparency in the oil and gas equipment rental industry. This field has been rarely addressed in previous studies. The findings demonstrate improved operational performance and stakeholder trust. However, further validation through real-world implementation and scalability testing is recommended for future research.
Comparative seismic performance of flat slabs using four design codes Samsul Abdul Rahman Sidik Hasibuan; Hakas Prayuda; Suraya Hani Adnan; Tengku Anita Raja Hussin; Fredy Kurniawan; Tika Ermita Wulandari
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.010

Abstract

This study evaluated the seismic performance of flat slab structures designed according to four international standards: SNI 2847-2019 (Indonesia), NZS 3101:2006 (New Zealand), IS 456-2000 (India), and JSCE 15-2007 (Japan). A five-story reinforced concrete building was modeled using ETABS for nonlinear pushover analysis, while MATLAB was utilized for calculating slab thickness, moment distribution, reinforcement design, and punching shear strength. Results unveiled that the JSCE 15-2007 code provided the stiffest system with minimal lateral displacement and the highest base shear capacity, whereas IS 456-2000 exhibited superior ductility and flexibility. SNI 2847-2019 and NZS 3101:2006 delivered balanced performance in terms of stiffness and material efficiency. The variation in slab thickness, reinforcement area, and natural period highlighted each code’s design philosophy regarding seismic safety. These findings suggest that code selection should consider regional seismic risk, structural demands, and construction priorities to ensure optimal safety and efficiency. The study contributes to the harmonization of global flat slab design practices and offers practical recommendations for improving the seismic resilience of buildings.
Vision-guided tracking and detection using the YOLOv5 model on a logistic delivery fixed-wing UAV Son Ali Akbar; Muhammad Taufiq Dinar Akbar; Anton Yudhana; Kamarul Hawari Ghazali
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.011

Abstract

Deep learning technologies utilizing Convolutional Neural Networks (CNNs) have advanced the development of autonomous systems, particularly in the exploration of hazardous environments. This study integrates the YOLOv5 object detection model with a fixed-wing Unmanned Aerial Vehicle (UAV) to identify simulated 5x5-meter orange marker dropping kits deployed in inaccessible disaster zones. Experiments were conducted at altitudes of 45 m, 75 m, and 100 m above sea level to assess real-time detection accuracy and terrain-mapping efficiency. The system achieved a mean Average Precision (mAP) of 88% across varying altitudes, demonstrating robust performance despite environmental challenges such as false positives from similarly colored rooftops. Computational efficiency tests were performed on the Jetson Nano platform using the TensorRT engine to accelerate object detection model inference on NVIDIA GPUs. Lighting variability significantly impacted detection reliability, resulting in a reduced mAP under suboptimal illumination. To enhance precision, post-processing filters and parameter optimizations were applied, improving the balance between detection sensitivity and specificity. These findings underscore the potential of YOLOv5-enabled UAVs for rapid, high-accuracy aid localization in disaster scenarios, although adaptive threshold tuning remains critical to address environmental variability in operational settings.

Page 1 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 30 No. 3 (2026) Vol. 30 No. 2 (2026) Vol. 30 No. 1 (2026) Vol 30, No 1 (2026) Vol 29, No 3 (2025) Vol 29, No 2 (2025) Vol 29, No 1 (2025) Vol 28, No 3 (2024) Vol 28, No 2 (2024) Vol 28, No 1 (2024) Vol. 28 No. 1 (2024) Vol 27, No 3 (2023) Vol 27, No 2 (2023) Vol 27, No 1 (2023) Vol 26, No 3 (2022) Vol 26, No 2 (2022) Vol 26, No 1 (2022) Vol 25, No 3 (2021) Vol 25, No 2 (2021) Vol 25, No 1 (2021) Vol 24, No 3 (2020) Vol 24, No 2 (2020) Vol 24, No 1 (2020) Vol 23, No 3 (2019) Vol 23, No 2 (2019) Vol 23, No 1 (2019) Vol 22, No 3 (2018) Vol 22, No 2 (2018) Vol 22, No 1 (2018) Vol 21, No 3 (2017) Vol 21, No 2 (2017) Vol 21, No 1 (2017) Vol 20, No 3 (2016) Vol 20, No 2 (2016) Vol 20, No 1 (2016) Vol 19, No 3 (2015) Vol 19, No 2 (2015) Vol 19, No 1 (2015) Vol 18, No 3 (2014) Vol 18, No 2 (2014) Vol 18, No 1 (2014) Vol 17, No 3 (2013) More Issue