Journal title : SINERGI Journal Initials : SINERGI Online ISSN : 2460-1217 Print ISSN : 1410-2331 Accreditation Status : Sinta 1 Accredited Journal, Decree No: 79/E/KPT/2023 Frequency : 3 issues per year (February, June, and October) DOI Journal : 10.22441/sinergi by Crossreff OAI Journal : https://publikasi.mercubuana.ac.id/index.php/sinergi/oai by OpenArchice.Org Editor-in-chief : Prof. Dr. Andi Adriansyah. Publisher : Fakultas Teknik, Universitas Mercu Buana Cite Analysis : Google Scholar, Sinta Indexing : Scopus; Google Scholar; Garuda Ristekbrin, DOAJ, BASE, and EBSCO.
Texture features-based automated classification for dental caries level images
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.
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Influence of water and oil quenching on the microstructure and mechanical properties of S45C steel
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
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
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
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
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
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
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
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
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
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.
Possibility of using immersion cooling technology with virgin coconut oil for split-type AC
Conventional split-type air conditioners are limited by high electrical consumption, noise from cooling fans, and bulky condenser coils that restrict installation flexibility. However, the application of immersion cooling in household AC systems remains largely unexplored. This study investigates the feasibility of using Virgin Coconut Oil (VCO) as a dielectric fluid for immersion cooling to improve energy efficiency and thermal performance. An experimental setup was developed using 20 L of SNI-compliant VCO, with the outdoor unit modified for immersion operation. Key performance parameters, including condenser coil and evaporator coil temperatures and the Coefficient of Performance (COP), were measured. Results showed condenser coil temperatures ranging from 40 °C to 58 °C and evaporator coil temperatures between 6 °C and 11 °C. The system achieved an average COP of 4.65 and reduced electrical consumption by approximately 20 %, indicating significant energy savings without compromising cooling efficiency. VCO also demonstrated suitable dielectric properties, with a breakdown voltage of 29.17 kV, near the IEC 156 threshold. These findings highlight immersion cooling with VCO as a promising, quiet, and energy-efficient alternative for household air conditioners. Further work is recommended to assess long-term reliability under varying operational conditions.
Taguchi-based optimization of activation parameters in activated carbon production for water pollutant adsorption
Water pollution from synthetic dyes, particularly methylene blue (MB), is known to pose serious environmental and public health challenges. Therefore, among various treatment methods, adsorption using activated carbon remained the most cost-effective and energy-efficient. In this research, activated carbon was prepared from Petung bamboo (Dendrocalamus asper), with the Taguchi method used to optimize the activation parameters. The following three factors: activation temperature (700°C, 800°C), holding time (40 min, 80 min), and activating gas (N₂, CO₂) were investigated based on an L4(23) orthogonal array design. The characterization process included proximate analysis, functional group identification, amorphous structure determination, and surface morphology examination. Optimization using the larger-is-better signal-to-noise (SN) ratio model aimed to maximize methylene blue adsorption. Additionally, analysis of variance (ANOVA) showed that activating gas, holding time, and temperature contributed 48.69%, 34.86%, and 8.06%, respectively, to adsorption performance, and all three parameters had a statistically significant influence on methylene blue adsorption. The optimal conditions, namely 800°C activation temperature, 40 min holding time, and N₂ gas, produced a maximum adsorption capacity of 9.275 mg/g. As a result, these findings showed that activated carbon derived from Petung bamboo was a sustainable and renewable adsorbent, offering a promising pathway toward environmentally friendly and cost-effective wastewater treatment technologies.
Topographic and hydro-oceanographic feasibility of a chemical port in Morowali
The industrial growth in Morowali Regency, Central Sulawesi, drives the need for port infrastructure to support the distribution of chemicals and fuel. This study aims to evaluate the technical feasibility of developing a liquid bulk terminal in Labota Village, Bahodopi District. The assessment involved hydro-oceanographic and topographic surveys, including a 15-day tidal observation, a 25-hour current measurement, and bathymetric and land surveys. The tidal analysis identified a mixed tide with semi-diurnal dominance and a tidal range of up to 2.66 meters. Current measurements recorded maximum surface speeds of 42 cm/s. Bathymetric data revealed varying depths up to -40 meters and an underwater mound at -10.21 meters, which may require dredging or navigational marking. The land topography is relatively flat and uniform, suitable for port development. Based on these findings, the site is considered feasible for the construction of a liquid bulk terminal capable of serving vessels up to 50,000 DWT with safe maneuvering and berthing operations. This research provides technical recommendations to support industrial logistics in the region, ensuring safe and efficient port operation while considering environmental sustainability. The study offers a comprehensive analysis of chemical and fuel terminal planning in Eastern Indonesia, contributing new insights to port infrastructure development research.
Activated carbon air filter and rubber seed oil approach from waste rubber seed shell for alternative fuel and improving air quality
Research related to rubber seed conversion to oil and activated carbon as filter media requires further exploration. Therefore, the main objective of this study to investigate the rubber seed oil as alternative energy and rubber seed Shell Activated Carbon (RSSAC). Thermo-chemical method conducted with separation process between the kernel and the shell. The process used temperatures of 550 and 600°C. Biodiesel was produced by a blending process using a frequency of 20kHz, temperature of 60oC and 2h holding time. In addition, the side product was converted into activated carbon through carbonization and activation using KOH. Air filter fabricated using three layers, where the top and bottom layers being non-woven and RSSAC in the middle. It compacted using hot-press method at temperature of 150°C for 60 s to produce an air filter media thickness of 3–5mm. The results show that there are several high compound concentrations i.e. CH4, aldehydes, and ketonestone. Several gases evolve, such as CO2, CO, CH4, H2O, ketone aldehyde, and HC. Microstructure analysis using Scanning Electron Microscope (SEM) of RSSAC shows that element C significantly increase up to 80%, while O, K, and Ca decreased up to 72%, 66% and 90%, respectively. RSSAC has a large surface area of 175.95m2/g, and it will have high effectiveness in improving indoor air quality (IAQ). This is indicated by the result of IAQ analysis where the humidity, temperature, CO, CO2, TVOC, and PM10 were lower than the acceptable limit of 70%, 27oC, 1000ppm, 10ppm, 3 ppm, and 0.15 mg/m3, respectively.
Experimental study of engine performance using a blend of RON 90 gasoline and fractionated gasoline equivalent from plastic pyrolysis oil
This study investigated the performance and emission characteristics of a gasoline engine using a blend of commercial gasoline RON 90 and fractionated plastic pyrolysis oil (PPO). Almost all previous studies used unfractionated PPO in engine performance tests. The use of raw PPO in engine performance tests will result in poor engine performance, because the physicochemical properties of the fuel do not meet engine requirements and the ASTM D4814 standard (gasoline fuel properties standard). An innovative aspect of this study is that the raw PPO was first fractionated to separate the gasoline PPO fraction, the diesel PPO fraction, and other aromatic fractions. Gasoline-equivalent PPO was used in the engine performance test to ensure the fuel used met engine specifications. PPO is obtained from post-consumer plastic waste through a pyrolysis process, followed by fractionation to separate heavy fractions and complex aromatic compounds. Blends containing up to 40% fractionated PPO were tested to evaluate their effects on engine performance and emissions. Experimental results showed that the use of 40% PPO only reduced thermal efficiency 0.79%, which is very low compared to the results of previous studies. In terms of emissions, the use of a 40% fractionated PPO blend reduced CO emissions by 7%, reduced HC by 17%, and increased CO2 by 17%. The reduction in CO2 and HC emissions is an innovative aspect of this study. These findings differ from previous studies using raw PPO, which reported significant engine performance degradation and increased emissions due to poor combustion characteristics.
Enhanced accuracy for classification modeling with a Decision Tree on rooftop solar PV
Rooftop photovoltaic (PV) systems play a crucial role in Indonesia’s decarbonization agenda; however, research on interpretable classification frameworks that integrate roof geometry and meteorological heterogeneity remains limited. Most previous studies have focused on module-level fault detection rather than assessing installation feasibility at the urban scale. Addressing this research gap, the present study proposes a transparent and data-driven approach using a Decision Tree (DT) model enhanced with Grid Search Cross-Validation (GSCV) to classify the feasibility of rooftop PV installations across 20 Indonesian capital cities. Simulation data generated by PVsyst incorporates multiple tilt and azimuth configurations, as well as local weather variables, representing one-, two-, and three-directional roof geometries. The proposed DT–GSCV model is benchmarked against k-NN, Gaussian Naïve Bayes, Logistic Regression, Random Forest, XGBoost, and CatBoost, demonstrating superior generalization and interpretability. Cross-location validation across five rotational subsets confirms stable performance, with an average accuracy of 91.0% ± 0.3 and an F1-score of 0.90, highlighting the model's robustness across diverse climatic zones. Feature importance and SHAP analyses reveal that irradiation and tilt angle are the most influential factors, while temperature and humidity negatively affect feasibility. The novelty of this work lies in developing a reproducible, interpretable machine learning framework that bridges physical PV modeling and data analytics for rooftop system design. This methodology enables rapid, transparent decision support for optimizing rooftop PV deployment across Indonesia's diverse urban and climatic settings.
Stability improvement of the Southern Sulawesi system using the Hippopotamus Optimization Algorithm (HOA)
This study proposes a Multi Band Power System Stabilizer (MB-PSS) optimized with the Hippopotamus Optimization Algorithm (HOA) to enhance dynamic stability in the Southern Sulawesi (Sulbagsel) electricity system integrated with wind power plants (WPPs). Unlike previous works applying HOA to distribution networks or photovoltaic optimization, this study addresses a key gap: the absence of HOA based stabilizer optimization in large scale multi machine systems, particularly in Sulbagsel. The novelty lies in positioning HOA as an alternative swarm intelligence method suited to the nonlinear characteristics of MB-PSS tuning. While the claim of being the first HOA application in Sulbagsel requires stronger justification, this study extends its relevance by bridging overlaps in related domains. Damping analysis and time domain simulations are conducted to benchmark HOA against Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). Results show that HOA achieves superior performance: overshoot in generator speed deviation decreases to 0.009923 to 0.002359 p.u., settling time reduces from 16s without stabilizer to 9.58s, and the maximum damping ratio increases to 0.7185. These outcomes confirm HOA’s capability to improve oscillation damping, though additional reporting of frequency responses and voltage stability metrics would strengthen the empirical contribution. Theoretically, this study highlights HOA’s balance between exploration and exploitation, making it suitable for multimodal cost functions in stabilizer tuning. However, broader theoretical implications, such as HOA’s advancement of stability theory beyond empirical results, remain underexplored. Future research should address this dimension to consolidate HOA’s role in advanced power system stability studies.
Pore-water pressure as the stress reference in the stress state variables for unsaturated soils, a theoretical revisit
Stress state variables refer to the state of stresses that control shear strength and volume change of unsaturated soils. When the pore-water pressure, uw, is taken as the stress reference, the stress state variables are expressed as (s - uw), (ua - uw), and uw for soils with compressible soil solids, and as (s - uw) and (ua - uw) for soils with incompressible soil solids. The existing theoretical derivation was based on the equilibrium equation of water, air, and contractile skin phases. However, several steps in the existing derivation are not clearly presented and are challenging to follow. These steps are in the initial stage of the equilibrium derivation and in the elimination of the third variable, uw, for incompressible soil solids. This paper presents revisit of the theoretical derivation to obtain the stress state variables. This revisit considers stress times area in place of force instead of stress times porosity in place of force as in the existing derivation. The proposed approach provides a clearer and more transparent procedure for eliminating uw from the governing equations when the solid particles are assumed to be incompressible. This revisit also provides sound stress state variables for unsaturated soils with both compressible and incompressible solids. Overall, the theoretical revisit in this paper offers clearer interpretation and a more comprehensible derivation. In addition, it supports the validity and correctness of the commonly adopted stress state variables used in the constitutive modelling of unsaturated soils.
A hybrid exploratory factor analysis - Grey Delphi framework for prioritization in occupational health and safety risks in the textile industry
The textile industry plays a vital role in supporting the national economy, but is characterized by complex and hazardous working conditions that pose serious challenges to occupational health and safety (OHS). Workers are frequently exposed to high-speed machinery, harmful chemicals, excessive dust, and physically demanding tasks, making risk identification and prioritization essential for improving workplace safety. This study aims to systematically identify and rank the most critical OHS risk factors by employing a hybrid methodology that integrates Exploratory Factor Analysis (EFA) and the Grey Delphi method. Data were collected from 390 textile workers and subsequently validated through the consensus of 12 experts. The EFA process reduced 57 initial indicators into nine underlying categories, while the Grey Delphi analysis prioritized 25 risks. Among these, the five most critical risks identified are: (1) excessive noise generated by weaving and spinning machines, (2) exposure to cotton dust containing endotoxins, (3) unprotected moving machine parts, (4) long working hours without adequate rest, and (5) improper or inconsistent use of personal protective equipment (PPE). The novelty of this study lies in integrating quantitative factor reduction with expert consensus under uncertainty, producing a replicable hybrid framework for data-driven OHS risk prioritization. This approach advances current literature by bridging statistical analysis with expert judgment, thereby improving methodological rigor. The findings provide measurable contributions for both scholars and practitioners by offering evidence-based guidance for policy formulation, resource allocation, and the design of targeted safety interventions to enhance OHS management in the textile sector.
Gradient-induced fuel consumption and CO₂ emission sensitivity: a comparative analysis of two and three-axle trucks on short uphill segments
Road freight transport significantly contributes to global energy use and greenhouse gas emissions, yet the influence of short uphill gradients on fuel consumption and CO₂ emissions remains insufficiently studied in Indonesia. While prior research has examined slope effects on long-haul operations, limited evidence quantifies sensitivity on short ascending segments (50–250 m) and compares two- and three-axle trucks. This study addresses this gap by applying a mathematical modeling framework that integrates technical parameters collected from the Datae Motor Vehicle Weigh Station (UPPKB Datae), Sidenreng Rappang Regency, South Sulawesi Province, Indonesia, with established fuel consumption and emission formulations. Gradient variations of 0–15% and travel distances of 50–250 m were analyzed, and fuel consumption results were converted into CO₂ emissions using IPCC guidelines. The findings reveal a sharp escalation in both fuel demand and emissions, with the slope increasing. At a 15% gradient and 250 m distance, CO₂ emissions rose by approximately 692% for two-axle trucks and 625% for three-axle trucks compared with the flat baseline. Although heavier trucks recorded higher absolute values, two-axle trucks exhibited greater relative sensitivity to changes in gradient. These results provide novel evidence on slope-induced inefficiencies in short segments, offering practical insights for eco-routing, operational planning, and gradient-sensitive decarbonization strategies in freight transport.
Benchmarking YOLOv8 and vision transformers for intelligent fish monitoring in aquaponics and controlled aquarium environments
Sustainable aquaculture requires reliable and accurate fish monitoring systems capable of operating across heterogeneous environmental conditions. Conventional monitoring approaches are labor-intensive and prone to human error, while recent advances in deep learning have enabled vision-based automation for aquatic environments. Convolutional object detectors such as YOLO and emerging Vision Transformer (ViT) models have demonstrated promising performance; however, most existing studies remain limited to single-environment evaluations and rarely address energy-constrained, real-world deployment. To bridge this gap, this study presents a systematic benchmark of YOLOv8 and ViT across two complementary settings: a controlled aquarium environment and a solar-powered, off-grid aquaponics system. The proposed framework integrates 1080p CCTV video acquisition, dataset annotation and augmentation, and standardized training and evaluation using COCO metrics. Experimental results show that ViT consistently outperforms YOLOv8 in detection accuracy and prediction stability across both environments. ViT achieves 99.73% accuracy in the controlled aquarium and ≥99.6% accuracy performance (99.68–99.73%) in aquaponics, while YOLOv8 records 87.90% accuracy in the aquarium and 93.92–97.92% across aquaponics fish classes, exhibiting higher sensitivity to background clutter. Statistical validation using McNemar’s test (p < 0.001) confirms that these differences are statistically significant. Beyond accuracy, the results reveal a trade-off between robustness and computational efficiency. ViT provides superior resilience under occlusion and glare, whereas YOLOv8 offers faster inference suitable for real-time operation on resource-limited edge devices. End-to-end deployment on a solar-powered NVIDIA Jetson Xavier NX demonstrates the feasibility of continuous, off-grid aquaculture monitoring and provides practical guidance for context-aware model selection in intelligent aquaculture systems.
Design and optimization of an electric screw propeller vehicle chassis using a BBNN-GA hybrid approach
Electric screw propeller vehicles represent an innovative solution for traversing difficult environments such as peat soil, particularly for fresh fruit bunch (FFB) transportation in the palm oil industry. However, the unique propulsion mechanism and demanding operational conditions impose significant structural challenges on the vehicle chassis, requiring a design that is both robust and lightweight to support electric vehicle efficiency. This study focuses on the design and optimization of a chassis for an electric screw-propelled vehicle for FFB transport operating on peat soil terrain. Advanced computational intelligence techniques, namely a Back Propagation Neural Network (BPNN) and a Genetic Algorithm (GA), are employed. The BPNN predicts key structural responses, including equivalent stress, fatigue life, safety factor, and weight, with high accuracy based on variations in material type and beam thickness. Furthermore, the GA utilizes these predictions to optimize the design. The optimized results show excellent agreement with finite element simulations, with deviations of only 3.47% in stress, 2.31% in fatigue life, 1.19% in safety factor, and 0.31% in weight, confirming the high predictive accuracy of the hybrid BPNN–GA model. The optimized chassis achieves a balanced trade-off between structural strength and light weight efficiency while remaining within allowable design limits. To the authors’ knowledge, this study represents the first application of a hybrid BPNN–GA approach for optimizing a screw-propeller vehicle chassis operating on peat soil terrain, offering a novel computational strategy for lightweight and reliable electric vehicle design in soft-terrain environments.
Comparison of Carbon Utilization Technologies for Decarbonization Strategy in the Ammonia Industry
Global climate change is caused by high greenhouse gas (GHG) emissions. The industrial sector is one of the most significant contributors of emissions, particulary from production activities and the use of fossil fuels. The ammonia (NH3) industry is an important chemical sub-sector that still relies on fossil fuels and contributes 1.6 tons of CO2 emissions per ton of ammonia production, accounting for nearly 2% of global carbon emissions. Therefore, Carbon Capture Utilization (CCU) technology is needed to support the decarbonization of the industry. This study uses Aspen Plus V14 software for process simulation. Three CO2 utilization pathways were simulated: methanol production (CH3OH), sodium bicarbonate production (NaHCO3), and methane production (CH4). The result show that converting CO2 into sodium bicarbonate (NaHCO3) yields the most favorable result with a profit of $65,277,360/year, a positive NPV of $840,647,028, an IRR exceeding the bank interest rate (10%) at 54%, and a POT in the fifth year. Additionally, sodium bicarbonate is environmentally sustainable, as evidenced by a CO2 emission reduction rate of 96%. The assessment was carried out under the assumptions of stable market conditions, sufficient availability of green hydrogen and ideal operating parameters, However, this study acknowledges inherent limitations, including catalyst performance, high energy requirements, and the challenge of integration with existing infrastructure, which may hinder large-scale implementation.
An adaptive decreasing sigmoid convergence factor for enhancing Grey Wolf Optimizer performance in high-dimensional optimization problems
Optimization algorithms require an effective balance between exploration and exploitation to achieve fast convergence and high solution quality. The Grey Wolf Optimizer (GWO) has demonstrated promising performance in various engineering applications; however, its conventional linear convergence factor often leads to premature convergence or insufficient exploitation, particularly in high-dimensional search spaces. To address this limitation, this study proposes an adaptive decreasing sigmoid convergence factor that dynamically regulates the transition between exploration and exploitation throughout the optimization process. Unlike the standard linear reduction scheme, the proposed sigmoid-based mechanism maintains stronger exploration during the early search stages and accelerates exploitation in later iterations through a controlled nonlinear decline. The proposed approach was evaluated using four widely adopted benchmark functions, namely Sphere, Rosenbrock, Rastrigin, and Griewank, under different dimensionalities, population sizes, and iteration limits. Experimental results demonstrate that the proposed method improves performance in most benchmark scenarios compared with the standard GWO. The best performance was obtained with a sigmoid parameter n = 0.75, which yielded near-optimal solutions for the Sphere and Griewank functions while maintaining stable convergence for the Rosenbrock function. The results further indicate that the proposed strategy scales effectively across medium- and high-dimensional optimization problems. These findings confirm that the adaptive decreasing sigmoid convergence factor provides a simple yet effective enhancement to GWO, offering improved convergence behavior and optimization accuracy across benchmark optimization problems.
Climate adaptive temperature correction for mitigating PV degradation in ASEAN climates
The emphasis on the ASEAN area is appropriate due to its unique tropical environment, characterized by elevated temperatures, high humidity, and little seasonal fluctuations. These features affect PV operating temperatures and degradation patterns in ways that current temperature correction models designed for other tropical areas inadequately address. In tropical regions, photovoltaic modules frequently operate at temperatures exceeding 35 °C, which is above the Standard Test Conditions of 25 °C. The conventional linear temperature correction (γ ≈ –0.45%/°C) fails to account for nonlinear thermal effects, leading to an underestimation of losses, battery under sizing, and a reduction in system lifespan. This study presents a nonlinear temperature correction model that incorporates a severity factor (δ) for cell temperatures exceeding 35 °C. The model utilizes two regimes: linear (≤35 °C) and quadratic (>35 °C) to account for nonlinear degradation. Simulations conducted at 45 °C and 1000 W/m² for a 100 WP panel indicate that the proposed model predicts an output of 90.575 W, compared to 81.9 W from the conventional model, resulting in an approximate 9.5% improvement in accuracy. This method addresses a significant gap by incorporating high-temperature nonlinearities, thereby enhancing the reliability of photovoltaic output predictions and improving battery sizing in tropical climates. This contribution enhances the reliability of photovoltaic systems and extends battery lifespan for applications in Indonesia and ASEAN.
Bitcoin price prediction using Autoencoder-based GRU and LSTM models
The high volatility of Bitcoin prices presents a major challenge in forecasting, as predictive models must capture both short-term fluctuations and long-term trends. Despite extensive studies on deep learning for cryptocurrency prediction, there remains a lack of systematic comparative analysis of autoencoder-based recurrent architectures, particularly AE-LSTM and AE-GRU, across both univariate and multivariate input settings, with statistical validation. This study analyzes Bitcoin price data from 2018 to 2025 to evaluate and compare the performance of two hybrid deep learning models, Autoencoder-based Gated Recurrent Unit (AE-GRU) and Autoencoder-based Long Short-Term Memory (AE-LSTM), in Bitcoin price prediction. The experiments explore the effects of dropout, learning rate, and epochs, using both univariate and multivariate inputs (Open, High, Low, Close). Results show that AE-GRU consistently outperforms AE-LSTM across all configurations, achieving up to 16.5% higher MAPE-based accuracy. The best performance was achieved by the multivariate AE-GRU, dropout = 0.1, learning rate = 0.001, epoch = 100, with RMSE 1667.125, MAE 1145.718, MAPE 2.33%, and R² 0.995017. Moreover, AE-GRU demonstrates faster training efficiency, requiring 185–195 ms/step, while AE-LSTM takes 208–215 ms/step under the same conditions. AE-GRU's superior accuracy and efficiency are attributed to its simplified gating structure and the feature compression capability of the autoencoder, which enhances learning stability and generalization. Overall, the AE-GRU model offers robust predictive performance and computational efficiency. It is a reliable framework for real-time cryptocurrency forecasting and a promising foundation for advanced deep learning architectures in financial time-series analysis.