Journal title SINERGI 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 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. SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by the Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high-quality theoretical and empirical original research papers closely related to the fields of Engineering (Mechanical, Electrical, Industrial, Civil, Chemical, 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 is registered in the CrossRef system with a Digital Object Identifier (DOI). The journal has been indexed by Scopus, Google Scholar, DOAJ, BASE, and EBSCO.
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Real-time Unmanned Surface Robot (USR) for river quality monitoring systemm
A real-time Unmanned Surface Robot (USR) for river water quality monitoring system is a technology that employs a small autonomous boat outfitted with sensors and other monitoring equipment to gather and transmit data on various water quality parameters like pH, temperature and total dissolved solids sensors in rivers and other bodies of water. The USR can traverse the river, gather information or data at specific points or designated locations, as well as continuously monitor a specific stretch of river at all times. The data or information was sent in real time to a central monitoring station, where it was analyzed and used to identify potential water quality problems. Initially, the USR was designed using SolidWorks software, and its structural performance was the main focus of the investigation and examination of the design. This USR was then created and manufactured. The entire USR system could help detect and mitigate pollution and other environmental problems, as well as offer useful information for managing water resources. Next, to determine the overall performance of the USR, five experiments and autopilot accuracy tests were performed. Finally, this study also verified and validated the accuracy of water quality monitoring sensors.
Copyright (c) 2026 SINERGI http://creativecommons.org/licenses/by-sa/4.0/
Performance Analysis of a Micro Underwater Remotely Operated Vehicle (ROV)
Underwater Remote Operated Vehicle (ROV) is a tethered marine robot that are widely employed for scientific and commercial applications. Several industries are working on underwater robots to increase the productivity, monitoring and surveillance especially in the petroleum and gas industries. These operations are often performed by human divers; however, the underwater environment poses hazards and pressure-related limits, making them costly and risky. As a result, ROVs have been designed to replace divers themselves. It is a tethered underwater robot that the operator controls manually using a PS2 controller. This project is to design and develop a micro underwater ROV for monitoring applications. The ROV are designed to withstand pressure underwater by selection of suitable material for its frame and other components will be equipped including pressure/depth sensor, MPU6050 IMU sensor and waterproof endoscope camera. Standard testing procedures are employed to assess the ROV's performance in buoyancy and control efficiency tests for the propulsion system in real environment, including laboratory pool. The developed ROV prototype shows promising performance with achieved 90% negative buoyancy is crucial for the ROV to perform effective submerge and raise operations and also with stable velocity and acceleration in forward, backward, and submerging. The steering tests highlighted that the ROV is more flexible and faster in maneuvering concerning turning performance as the horizontal thrusters’ configurations are positioned at 45° at the back of the ROV. The outcomes of this project are anticipated to bring substantial advantages to industries associated with underwater applications.
Compound development as a protective layer on fecral substrate by a combination of γ-Al2O3 ultrasonic and NiO electroplating techniques to improve thermal stability
One of the most technologically advanced methods for developing and adhering catalysts to the FeCrAl substrate is electrophoretic deposition. However, it faces a problem: low thermal stability at high temperatures of 10000 °C, caused by a lack of a protective oxide layer. The goal of this study is to investigate the protective oxide layers formed by Al2O3 and NiO coatings on FeCrAl metallic material for catalytic converters (CATCO). The electrolyte was prepared with distilled water at a constant temperature of 40±50 °C. The pH was adjusted to 5 with HCl and NaOH reagents. The electrolyte was prepared at 40 ± 50 °C and stirred for 1 minute using a magnetic stirrer. A 50mm x 10mm Ni plate substrate served as the anode, while a 40mm x 20mm FeCrAl cathode was used. The spacing between the anode and cathode was set at 25mm. The electroplating was conducted for several variation times of 15, 30, 45, 60 and 75 minutes, current density of 8 A/dm2, 3g γ-Al2O3 was inserted into the beaker for each sample and the total surface area was 1600mm2 on both sides. Drying was performed after electroplating at 600 °C for 12 hours. Raman spectroscopy revealed that several compounds observed during the experimental stages, such as FeCrAl, γ-Al2O3, NiO, NaO2, NiAl2O4, NiCr2O4, and FeCr2O3, were also present in the coated FeCrAl CATCO, with distinct peaks. Therefore, it can be concluded that the UB+EL 30 min successfully deposited the γ-Al2O3 and NiO on the FeCrAl substrate after CATCO fabrication.
Land cover changes, built-up and vegetation density, and the Urban Heat Island (UHI) phenomenon in Pekanbaru City
Pekanbaru city has a high population growth rate and is currently experiencing rapid urbanization, which is driving urban expansion. Urban development alters land cover patterns and reduces environmental quality. The development of residential areas and infrastructure reduces vegetation, affecting Land Surface Temperature (LST) and contributing to the emergence of the Urban Heat Island (UHI) phenomenon. This study aims to analyze changes in land cover, examine the correlation between LST and the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI), and then investigate the UHI phenomenon in Pekanbaru City. The research method is quantitative, using data from Pekanbaru City, an administrative map, and Landsat 8 OLI/TIRS imagery, which were spatially analyzed in ARGIS and QGIS. The novelty is the use of guided classification and maximum likelihood algorithms for land cover classification, which revealed significant changes over the five years from 2018 to 2023 in Pekanbaru City. Over 5 years, land cover in the city of Pekanbaru changed, with water bodies increasing by 23%, palm areas increasing by 5%, built-up areas increasing by 34%, and vegetation increasing by 10%, while bare land decreased by 57%. There are significant changes in built-up and vegetation density. The correlation between land surface temperature and built-up density is positive; however, it is negatively correlated with vegetation density. There is an urban heat island phenomenon in Pekanbaru City, characterized by surface temperatures exceeding the UHI threshold.
Copyright (c) 2026 SINERGI https://creativecommons.org/licenses/by-sa/4.0
Sustainable competitiveness through community empowerment and the success factors of SMEs?
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 Examination of the Fe₃O₄ nanomaterial impact in conjunction with Magnetorheological Elastomer material
Magnetorheological elastomer (MRE) is an advanced material class that can be used for vibration damping. This material possesses the ability to reduce vibration disturbances through adjustment of its mechanical properties in response to a magnetic field applied from an external source. The objective of this study is to ascertain the effect of incorporating Magnetite (Fe₃O₄) nanomaterials into MRE. It is expected that this new material will be more sensitive to magnetic fields in damping vibrations, which would be a significant improvement. MRE is composed of carbonyl iron powder (CIP), silicone oil, and silicone rubber, with weight proportions of 30%, 5%, and 65%, correspondingly. The addition of magnetite nanomaterials to MRE occurred at weight ratios of 0.5%, 1%, 1.5%, and 2%. Observations of this new material included elemental composition analysis and viscoelastic testing of various mixture formulations in the laboratory. From this research, it can be concluded that an MRE containing Fe₃O₄ nanomaterials has been created. For the attenuation of vibrations within the 1–100 Hz frequency range. MRE-2 (MRE with 0.5% Fe₃O₄ added) is the best choice as the primary material, as it exhibited the highest tan delta value and strong damping performance at an intermediate frequency. MRE-1 sample was used as a base material mixture without added Magnetite also an excellent choice, offering high stiffness and good damping capability at low frequencies. It is shown by the results of this experiment that the effectiveness of MRE in reducing vibration can be increased by adding Magnetite, even in the limited mid-frequency range of 0 to 100 Hz.
Determination of critical factors and the best alternatives for developing biodiesel from Maggot BSF
This paper explores the approach for producing biodiesel from Maggot Black Soldier Fly (BSF) as a sustainable renewable energy source in Indonesia. The SWOT and VIKOR techniques determine the most effective strategy for promoting renewable energy in Indonesia. The paper included numerous respondents to ascertain the criteria and assess each option. Environmental consciousness is an important strong component in biodiesel development, with a value of 1.52. A significant drawback in biodiesel production is the elevated investment costs, quantified at 1.48. A notable opportunity in biodiesel development is its potential as an environmentally sustainable energy alternative, scoring 1.32, while a considerable threat is inadequate financial assistance, scoring 1.24. Moreover, applying the VIKOR approach reveals that alternative 6 (Enhancing collaboration among stakeholders) is the most critical option, as expert evaluations indicate, with a value of 0.048. The outcomes of this study require enhancement since additional research is necessary to yield more precise findings that will augment our comprehension of the evolution of renewable energy in Indonesia. Future studies should focus on the ramifications of producing biodiesel from BSF maggots, particularly in terms of energy security and energy autonomy in Indonesia.
An ultra-broadband microstrip antenna using a triple dumbbell-shaped defected ground structure
Microstrip antennas are widely used in modern communication systems due to their compact size and low profile. However, they typically suffer from narrow bandwidth, limiting their performance in advanced wireless applications. This study addresses this limitation by employing a triple dumbbell-shaped defected ground structure (DGS). The antenna is designed to operate at 3.5 GHz using a Rogers RT5880 substrate, and its performance was analyzed through simulations in HFSS 15.0 software. Without the DGS, the antenna exhibits a fractional bandwidth (FBW) of only 1.71%, operating from 3.47 GHz to 3.53 GHz. Incorporating the triple dumbbell-shaped DGS in the ground layer increases the FBW significantly to 53.6%, extending the operating frequency range from 2.39 GHz to 4.14 GHz. This improvement was achieved through the careful optimization of DGS parameters. The simulated gain at 3.5 GHz is 5.13 dBi. The proposed design demonstrates superior performance compared to conventional techniques such as split-ring resonators (SRR) and Butler matrix (BM) configurations. Simulation and measurement results show excellent agreement, validating the design. The achieved ultra-wideband performance benefits 5G and next-generation systems, offering greater frequency tolerance, diverse signal support, increased capacity, and reliable operation, making the antenna a promising candidate for future wireless applications.
Experimental investigation of PWHT and normalizing effects on SMAW low-carbon steel joint properties
The influence of post-weld heat treatment (PWHT) followed by normalizing on the mechanical properties of AH36 low-carbon steel is significant, particularly in the context of marine applications, such as shipbuilding welded joints. According to the extant literature, PWHT has been demonstrated to reduce residual stresses and enhance microstructural uniformity. However, the suitable PWHT temperatures for AH36 steel welds to balance strength, ductility, and toughness prior to normalizing remain underexplored. The objective of this study is to ascertain the suitable PWHT temperatures prior to normalizing, with the aim of improving weld performance in marine environments. A parametric study was conducted on AH36 steel specimens welded using shielded metal arc welding. The specimens were subjected to PWHT at 0°C (as-welded), 450°C, 600°C, and 750°C, followed by normalizing. Tensile, bending, and Charpy impact tests were utilized to assess the mechanical properties against established maritime safety standards. The results show that 600°C is the optimal PWHT temperature, effectively reducing residual stresses and promoting microstructural homogeneity. This, in turn, ensures that welds meet safety standards while preserving mechanical integrity. Higher temperatures increased the risk of brittleness, while lower temperatures provided insufficient stress relief. This study demonstrates that precise selection of PWHT temperature prior to normalizing is critical for ensuring reliable welds in marine structures. It identifies the optimal condition that maximizes strength, ductility, and impact toughness of AH36 steel while satisfying the Indonesian Classification Bureau (BKI) maritime safety standards.
The effect of different levels of cleanness of the pre-coat surface on adhesion and corrosion performance of A36 steel with epoxy coating
Adhesion and corrosion protection are the main properties of epoxy coatings, especially when applied to materials exposed to harsh environments, such as chloride-containing water. However, the adhesion and corrosion protection of coatings are affected by surface preparation, especially the cleanliness of the substrate surface prior to coating application. Choosing the proper surface preparation can optimize the coating's capabilities. This research aims to evaluate the Effect of blasting process cleanliness on coating performance on the steel surface. The novel approach is to correlate NACE surface cleanliness standards with coating performance. In this study, A36 steel is used. The cleaning procedure uses an air-blasting process with an 8-bar nozzle pressure and at least 5 minutes of spraying time to meet the desired National Association of Corrosion Engineers (NACE) standard. The abrasive utilizing garnet with a mesh of 30-40. Meanwhile, coating is performed at room temperature using the airless spray method with a 90° angle, a distance of 25 cm from the substrate and the nozzle, and a nozzle speed of 300 mm/s. The gap in the coating process between the first and second layers is 24 hours. The results showed that surface preparation influenced the coating's pullout strength and corrosion performance. The pullout strength test demonstrated that NACE 2 provided the highest pullout strength. Likewise, corrosion rate testing showed that surface preparation affects the corrosion rate, with NACE 1 providing the lowest corrosion rate (the best corrosion protection).
Risk-based predictive maintenance of medium voltage network switching equipment using analytical hierarchy process as an analytical tool
Predictive maintenance has become crucial for enhancing the reliability and efficiency of electrical systems, especially for Medium Voltage Network (MVN) switching equipment, which plays a key role in electricity distribution. This study aimed to develop a risk-based predictive maintenance model for MVN switching equipment using the Analytical Hierarchy Process (AHP) for maintenance prioritization, along with Z-score and Monte Carlo simulation methods to evaluate risk likelihood and impact. The Z-score method assessed the probability of risks occurring, revealing a probability exceeding 90% for specific equipment, such as UP2D.2025.C4, at 93.12%. The Monte Carlo simulation assessed the potential impact of these risks, showing severe consequences for various types of equipment. For example, UP2D.2025.C1 had a mean of 28.51 and a standard deviation of 3.50, while UP2D.2025.C8 had a standard deviation of 33.17, with an impact of over 61.53%. AHP was used to assign priority weights to components based on criteria such as equipment age, operational condition, and failure history. The analysis indicated that the Lightning Arrester had the highest maintenance priority at 26.04%, followed by the Fuse Cutout at 20.62% and the Pole-Mounted Circuit Breaker at 11.15%. This research was expected to significantly contribute to the development of more efficient and effective maintenance strategies for electrical systems, particularly in the electricity distribution sector.
Shear strength enhancement of fine sand soil using Guar Gum biopolymer under varying curing conditions
This study investigates the effect of Guar Gum biopolymer on the shear strength behaviour of fine sand soil, with the aim of evaluating its potential as a sustainable soil stabilization agent. A series of direct shear tests, following ASTM D3080-23, was conducted on Guar Gum-treated soil samples with varying biopolymer concentrations (1%, 3%, and 5%) and water content (10%, 12%, and 15%). Curing durations of 2, 5, and 7 days were applied to assess time-dependent strength development. The shear strength parameters, cohesion (c) and internal friction angle (φ), were evaluated to quantify the improvement in soil performance. The results showed that cohesion increased with higher Guar Gum concentration and longer curing times, with the highest cohesion (0.105 kg/cm²) observed at 5% concentration after 7 days. However, the internal friction angle decreased with prolonged curing, suggesting a shift from the frictional to cohesive strength. Water content had a significant impact, with 10–12% yielding optimal results. At a water content of 12 %, the highest internal friction angle (52°) was recorded after 7 days. Overall, the findings confirm that Guar Gum can significantly enhance the shear strength of fine sand when key parameters are optimized, offering an effective, environmentally friendly alternative to conventional chemical stabilizers in geotechnical applications.
Dynamic modeling of lithium-ion battery degradation using data-driven and physics-informed method
Accurate real‑time prediction of lithium‑ion battery (LIB) capacity degradation is essential for embedded battery‑management systems. Equivalent circuit models (ECMs) run quickly but lose accuracy over time, whereas purely data-driven networks achieve high precision at a high computational cost. This study introduces a physics‑informed neural network (PINN) that embeds the differential equations of a first‑order Thevenin ECM directly into the loss function. Using only terminal voltage and current as inputs, the network simultaneously estimates internal resistance, polarization resistance, polarization capacitance, open‑circuit voltage, and capacity loss. The model was trained and evaluated over 300 charge–discharge cycles of a 18650 lithium-ferrous phosphate (LFP) cell. The resulting capacity degradation estimation achieved a root mean squared error (RMSE) of 0.012 and a mean absolute percentage error (MAPE) of 0.974 %, surpassing a neural ordinary differential equation baseline with RMSE of 0.215. The trained network contains 261 parameters, requires 0.6 ms per sample for inference, and consumes 49 MB of memory. This computation cost is far lower than that of a long short‑term memory (LSTM) benchmark with comparable accuracy. In addition, the proposed model maintains its accuracy under limited dataset conditions. With a fourfold smaller training set, the PINN maintained an RMSE of 0.023, whereas the LSTM error increased to 0.72. The results demonstrate that lightweight neural networks guided by physics-based constraints can provide reliable, real-time health estimation on resource‑limited hardware.
A predictive safety and maintenance framework for railway locomotives: integrating HAZOP, FMEA, and IoT-based risk mitigation
Safety and maintenance efficiency are critical challenges in the railway industry, particularly in the use of lifting jacks for locomotive maintenance. This study proposes a predictive maintenance framework that integrates the Hazard and Operability Study (HAZOP), Failure Mode and Effects Analysis (FMEA), and Internet of Things (IoT) technology to detect potential failures in real time. A case study was conducted at a locomotive maintenance depot in Indonesia, where several occupational accidents had been recorded due to lifting jack malfunctions. Based on HAZOP and FMEA analyses components such as stoppers and drive motors were identified as having high Risk Priority Numbers (RPN), each reaching 512, indicating significant failure risks. The proposed IoT system employs HCSR-04 and MPU6050 sensors to accurately monitor the height and inclination of the equipment. Evaluation results show that the system effectively detects anomalies with minimal data deviation and a low data loss rate during a 10-day testing period. The implementation of this system significantly reduces workplace accident risks, improves maintenance efficiency, and supports digital transformation within the industrial environment. These findings demonstrate that the integration of HAZOP, FMEA, and IoT is effective for risk mitigation and can be replicated in other railway components. Moreover, this research opens new avenues for developing AI-based predictive systems and implementing digital twins as part of future smart maintenance strategies.
The impact of the inclination angle of perforated screen facade on daylight performance in the tropics
Daylighting is one of the fundamental aspects of green building principles. Utilizing daylighting in a building offers numerous benefits, including energy efficiency, enhanced comfort, improved workplace productivity, better health, and increased economic value. However, buildings with glazed facades can experience excessive illuminance, uneven daylight distribution, and glare without proper shading devices. Perforated screen facade (PSF) is one of the shading devices widely used in buildings with glass facades. PSF minimizes direct solar radiation and enhances daylighting performance while preserving outdoor views. This study focused on one design variable of PSF, the inclination angle, which had not been widely explored in previous research within the context of a tropical climate. The research aimed to evaluate the impact of the PSF inclination angle on daylight performance. The research method was experimental, using radiance-based simulation as a tool. The daylight availability and visual comfort of office buildings with vertical PSF were compared with inclined PSF. The daylight performance metrics analyzed included mean illuminance, useful daylight illuminance, and spatial disturbing glare. The results indicated that implementing an inclined PSF resulted in mean illuminance ranging from 1065 to 1105 lx, useful daylight illuminance between 95.08% and 95.55%, and spatial disturbing glare between 5.1% and 6.5%. Increasing the PSF inclination angle raises the mean illuminance and spatial disturbing glare and reduces the useful daylight illuminance. PSF can be applied with an inclination angle to buildings in the tropics, providing broader possibilities for facade design exploration.
Towards enhanced acoustic fan booster damage detection: a comparative study of feature-based and machine learning approaches
Machine failure detection frequently uses non-destructive monitoring techniques such as vibration analysis. Although vibration analysis can identify machine degradation, the apparatus is often costly and necessitates specialist knowledge. Additionally, many existing methods in audio classification rely on characteristics represented as pictures or vectors, which increases computational complexity. In contrast, this research introduces a novel method that substitutes vibration data with a singular numerical feature derived from audio signals, addressing both cost and complexity issues. Our objective is to develop a rapid and precise audio-based method for detecting machine damage. The acoustic signals from the machine apparatus were classified into three categories: normal, belt damage, and combined belt and bearing defect. The data processing technique involved lowering the sample rate and segmenting the data to improve computational efficiency and classification performance. We use the Welch method and appropriate statistical techniques to analyze Power Spectral Density (PSD). The performance of seven classifier models, KNN, LDA, SVM, NB, ANN, RF, and DT, was evaluated using accuracy, precision, sensitivity, specificity, and F-score. LDA achieved the highest accuracy at 92.83%, followed by ANN (92.75%), NB (92.74%), and DT (92.34%). These models outperformed KNN (89.90%) and RF (89.40%), with SVM recording the lowest accuracy at 85.40%. LDA was highly effective, achieving the highest accuracy with a single average PSD-type feature, showcasing its robustness in machine defect diagnosis. Compared to previous methods, this approach simplifies feature extraction, reduces computational demands, and maintains high diagnostic performance, providing notable benefits in terms of effectiveness and precision.
An effective and efficient vehicle detection using ER-EMA-YOLOv10n
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.
Development and performance evaluation of an automatic size-sorting system for catfish seeds using photodiode sensors
In catfish farming, uniform seed size is crucial for ensuring balanced growth and minimizing competition for feed. Generally, size sorting is performed manually through visual observation and net separation, which is labor-intensive, time-consuming, and often causes stress or injury to fish. To address these limitations, this study aimed to develop and evaluate a real-time, low-cost automatic sorting system for live catfish seeds. The proposed system utilizes photodiode sensors and an Arduino-based microcontroller to detect variations in fish body length by interrupting a laser beam. Four photodiodes were arranged at specific distances to classify fish seeds into four size categories (<7 cm, 7–8 cm, 9–10 cm, and 11–12 cm). After classification, the system automatically directed each seed into the corresponding container. The results showed that the prototype successfully classified and sorted catfish seeds with an overall accuracy of 67.5%. In contrast, tests with PVC pipes under controlled conditions achieved 100% accuracy. These findings highlight the novelty of integrating size detection and direct sorting for live fish seeds, a feature not previously reported in the literature. Beyond its current limitations, this system provides a methodological framework for sensor-based aquaculture automation, offering potential for further improvements in accuracy, robustness, and application to other aquaculture species.
Real-time deep neural network-based waste detection and classification using a camera sensor
Waste generation is a growing environmental concern, with manual sorting methods often being inefficient and error-prone, particularly under varying lighting and environmental conditions. In Indonesia, waste is typically categorized into organic and nonorganic, yet existing automated classification systems lack real-time capabilities and robustness in dynamic settings. This study proposes a novel real-time waste detection and classification system using a deep neural network, implemented on the Jetson Nano platform with a camera sensor. The system utilizes the ResNet-18 convolutional neural network architecture and is developed using Python. It is designed to distinguish between organic and nonorganic waste in real-time. Training was conducted over 30 epochs, and the system was tested under various lighting conditions—morning, daytime, afternoon, and nighttime. Results show high accuracy: 95.24% in the morning, 95.24% during the day, 90.45% in the afternoon, and 86.90% at night, with an average accuracy of 91.96%. Performance was influenced by factors such as lighting intensity, distance, waste position, changes in organic waste, and occlusion by plastic. The proposed system offers a significant improvement over traditional and existing methods by enabling accurate, real-time waste classification under diverse conditions, contributing to more efficient and intelligent waste management.
Enhanced Classification of Multi Abnormal Brain Tumors Detection Using Customized Inception V3
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.
Towards low-carbon ammonia: simulation and economic evaluation of blue ammonia with carbon utilization
The proposed blue ammonia production considers technical, environmental, and economic aspects. The design of the blue ammonia using CCUS (Carbon Capture, Utilization, and Storage) technology in this study contributes to reducing carbon emissions and providing a more environmentally friendly ammonia supply in East Java, Indonesia, due to the availability of raw materials and geological storage locations for CO2 storage. Technically, the blue ammonia production was simulated with Aspen Hysys V.14.0. uses the Kellogg process, where the ammonia converter operates at a temperature of 437.60 °C and a pressure of 141.9 bar. From the environmental aspect, as much as 68.34 tons/h of ammonia produced produces CO2 71.36 tons/h, which is a total emission of 1.06 tons CO2/ tons NH3. In this study, CO2 delivery with a pipe length of 85 km (ID:539.8mm; OD: 558.7mm) was simulated using default parameters in Aspen Hysys V.14.0. In economic calculations from APEA (Aspen Process Economic Analyzer), the manufacture of blue ammonia designed in this study is very large, with a TAC (Total Annual Cost) of $82.25x106/year and an LCOA (Levelized Cost of Ammonia) of $93.28x108/ tons NH3. This study demonstrates the integration of CCUS technology into ammonia production, resulting in a reduction of CO₂ emissions by 1.06 tons CO₂ per ton of ammonia produced. The proposed system provides a practical approach for improving the environmental sustainability of industrial chemical processes.
Use of activated carbon from NiO modified Polyethylene Terephthalate plastic bottle waste to optimize natural gas storage in Adsorbed Natural Gas (ANG) technology
Storage and transportation of natural gas are major challenges in optimizing energy use. To overcome the challenges, Adsorbed Natural Gas (ANG) technology offers a promising alternative for increasing storage capacity at lower pressures. Therefore, this study aims to explore the efficiency of waste polyethylene terephthalate (PET) bottles converted into activated carbon through pre-treatment, carbonization, chemical activation with 4 M KOH, and physical activation using N₂ flow. Modification of activated carbon was carried out using NiO metal impregnation at concentrations of 0.5%, 1%, and 2% to enhance adsorption performance. The results of characterization using iodine number, scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS) showed that the 2% NiO-impregnated sample had the highest surface area of 997.65 m²/g. Natural gas adsorption and desorption testing showed that this material achieved the maximum storage capacity of 138.9 g/kg at 28°C and 9 bar, with superior performance compared to non-impregnated samples and several previously reported ANG adsorbents. These results showed that combining NiO modification with KOH-activated PET waste improved methane uptake beyond commercial activated carbons and provided an environmentally sustainable solution for plastic waste valorization.
Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters
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.
Utilization of teak wood powder waste as eco-friendly filler in HRS-WC asphalt: a comparative analysis of dry and wet Marshall mix methods
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.
Stress-strain behavior and residual strength of Petobo sand with variable fines content under consolidated undrained triaxial loading
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.
3D numerical investigation of roadway bridge response under hydrodynamic forces and local scour in stiff clay and sand foundations
Bridges are critical components of transportation networks but are highly vulnerable to failure during extreme flood events, particularly due to hydrodynamic forces and local scour. This study quantitatively evaluates the effects of flood velocity and scour depth on bridge pier displacement for two representative soil conditions: very stiff clay (Ground Type B) and medium-dense sand (Ground Type C). A 3D finite-element model incorporating non-linear p–y springs was developed in CSI Bridge to represent soil–structure interaction (SSI). A total of 192 simulations were performed across flood velocities of 2–16 m/s and scour depths ranging from 0DF to 2DF. The results show that pier displacement increases systematically with both velocity and scour, with medium-dense sand exhibiting up to 30% higher displacement than very stiff clay at severe flood conditions (0.07 m vs. 0.06 m). These findings highlight the importance of soil stiffness in governing pier response under extreme hydrodynamic loading. While the study does not address debris impact, flow directionality or additional hydraulic parameters, the outcomes provide valuable insight for improving foundation design and incorporating SSI considerations into flood-resilient bridge engineering.
Overestimation of load-resisting capacity in double-span welded steel beams: a comparative FEM study incorporating ductile damage and element deletion
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.