Advance Sustainable Science Engineering and Technology
Journal Title Advance Sustainable Science, Engineering and Technology (ASSET) Frequency 4 issues per year (January, April, July, October) DOI Prefix 10.26877 by ISSN 2715-4211(Online) Editor-in-chief Mega Novita, S.Si., M.Si., M.Nat.Sc., Ph.D Publisher Universitas PGRI Semarang Indexing SCOPUS| Sinta 1 | Dimensions | Crossref | Garuda | Google Schoolar | Scimagojr (Q4) This page is a new page for an old journal https://journal.upgris.ac.id/index.php/asset Advance Sustainable Science, Engineering and Technology (ASSET) is a peer-reviewed open-access international scientific journal dedicated to the latest advancements in sciences, applied sciences and engineering, as well as relating sustainable technology. This journal aims to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of sciences, engineering, and technology. ASSET papers will be published twice a year by Universitas PGRI Semarang, Jawa Tengah, Indonesia.
Science and Technology Research Centre Universitas PGRI Semarang
GIS-Based Spatial Equity Assessment of Temporary Waste Disposal Sites in Riverbank Informal Settlements: A Case Study of Palembang, Indonesia
Rapid population growth and urbanization in informal settlements along the Musi Riverbank, Palembang City, have intensified pressure on the waste management system, particularly due to the uneven distribution of Temporary Disposal Sites (TPS) and limited collection services. This study, conducted in June–July 2025, examines the spatial conditions of TPS and waste management through a quantitative methods approach that integrates Geographic Information Systems (GIS) with a survey of 385 respondents across 13 sub-districts. Results indicate that TPS facilities remain concentrated in densely populated areas, while Gandus and Kertapati exhibit substantial service gaps, with 63.1% of residents unaware of any TPS near their area and 20.3% reporting inadequate TPS availability. Spatial analysis shows that only 34.0% of households live within 0–0.5 km of a TPS, while 24.4% are located more than 1 km away, indicating significant underserved zones. The study recommends GIS-based TPS redistribution and stronger community engagement mechanisms as actionable strategies to support evidence-based municipal waste planning and policy.
Copyright (c) 2025 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Effect of NaOH Molarity on Gel Formation at the Geopolymer–Artificial Aggregate Interface for Pavement Applications
This study investigates the effect of NaOH molarity (10, 12, and 14 M) on gel formation at the geopolymer–artificial aggregate interfacial transition zone (ITZ) for sustainable pavement binder courses. The study focuses on microstructural and chemical characteristics rather than direct mechanical performance. Using XRF, FTIR, and SEM–EDX analyses, the evolution of gel phases from N-A-S-H to hybrid N/C-A-S-H at the interface was systematically characterized. Increasing NaOH molarity enhanced aluminosilicate dissolution and promoted Ca incorporation, as evidenced by the shift of the main Si–O–T band from approximately 1080 cm⁻¹ (10 M) to 960–970 cm⁻¹ (14 M) and the increase in Ca/Si ratio from 0.15 to 0.22. Among the investigated mixtures, the 12 M variant exhibited the most homogeneous and compact ITZ microstructure, characterized by a continuous reaction rim, balanced Si/Al (2.96) and Ca/Si (0.22) ratios, and minimal microcracking observed at the microscopic scale.
Integrated 1D-2D Resistivity Inversion for Mapping Seawater Intrusion in a Coastal Aquifer: Kenjeran-Surabaya, Indonesia Case Study
This study investigates the extent of intrusion in the Kenjeran coastal aquifer, Surabaya, Indonesia, through an integrated geophysical approach. Four one-dimensional Vertical Electrical Sounding (VES) surveys and two two-dimensional Electrical Resistivity Tomography (ERT) transects were conducted using Schlumberger and Wenner–Schlumberger configurations to obtain both depth-specific and laterally continuous resistivity data. The 1D VES results detected low-resistivity layers (<1.0 Ω·m) at depths exceeding 58–66 m, indicating deep saline groundwater. The 2D ERT sections identified wedge-shaped low-resistivity anomalies (0.1–0.8 Ω·m) at depths of 7.5 m to 48 m, indicating active intrusion progressing inland. Intrusion is more severe in the northern sector, with vertical penetration up to 48 m and horizontal encroachment beyond 200 m from the shoreline. The integration of 1D and 2D resistivity imaging proved effective in delineating saline–freshwater interfaces, enabling targeted mitigation measures and informed groundwater management to safeguard Kenjeran’s aquifer from further degradation.
Kernel Density and Spatial Modeling of Informal Settlement Concentration: Methodology and Findings from Palembang, Indonesia
Rapid urbanization has intensified the growth of slum settlements in Indonesian cities, including Palembang, where informal housing commonly develops along riverbanks. This study aimed to identify and evaluate the spatial distribution and density of slum areas in Palembang City through a Geographic Information System (GIS)–based approach combining Kernel Density Estimation (KDE) and Receiver Operating Characteristic–Area Under Curve (ROC–AUC) analysis. Primary spatial data were obtained from 382 household survey points representing 64 slum polygons across 13 sub-districts, supplemented by administrative boundary and land-use data from the Palembang City Government. Spatial analysis and validation were conducted using ArcGIS 10.3 software. The KDE results showed density values ranging from 0 to 58.1123 units per 100 m², with the highest concentrations found along the Musi River corridor, decreasing outward from the riverbanks. Model validation achieved an AUC value of 0.968 (96.8%), demonstrating excellent predictive accuracy. These spatial outcomes provide actionable guidance for policymakers by identifying priority zones for sanitation and drainage upgrades, flood-resilient housing design, and targeted relocation planning. The study highlights the practical role of GIS-based quantitative modelling in supporting evidence-based slum management and urban infrastructure planning in Indonesia.
Uncertainty-Quantified Grid-Convergence Analysis of RANS Turbulence Models for 2-D Incompressible Backward-Facing Step Flow in OpenFOAM
A concise evaluation of Reynolds-Averaged Navier–Stokes (RANS) turbulence modeling for two-dimensional, incompressible, steady backward-facing step (BFS) flow at Re = 1000–3000 was conducted using OpenFOAM’s SimpleFoam solver with the standard k–ε model. A tri-level mesh enhancement (coarse, medium and fine) was implemented, and ambiguity was measured utilizing the Convergence Ratio (CR) and Grid Convergence Index (GCI). The fine grid (CR = 0.54; GCI = 0.0059%) was the only configuration exhibiting monotonic convergence, ensuring valid GCI estimation. Results showed reattachment length increasing from 0.11 m to 0.12 m, with stronger vortical structures and steeper shear gradients at higher Re. This study uniquely integrates RANS model validation with grid-uncertainty quantification, providing guidance for mesh optimization and reliable turbulence modeling in BFS simulations.
Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
Graph-Theoretical Approach to Clusters – Analysis of Clusters with 5–11 Vertices –
In this study, we systematically analyzed all isomers of 5–11 atom clusters using a graph-theoretical approach and evaluated their maximum Hückel energy (HE) within the Hückel approximation. The maximum HE increased with the number of bonds, reached a maximum at an intermediate value, and then decreased again, with the peak located near the average of the minimum and maximum possible edge numbers. This relationship was well reproduced by a simple linear expression. The isomers with maximum HE were found to have nearly uniform degree distributions, and classification by graph radius (Gr) and maximum degree (Ma) revealed that they possess compact and homogeneous structural characteristics. By extrapolating these trends, structural features of maximum-HE isomers were predicted for clusters with 12–14 atoms. These results demonstrate the effectiveness of a graph-theoretical description for analyzing cluster stability and provide a foundation for extending the analysis to larger clusters and for comparison with first-principles calculations.
Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features
Cognitive workload plays a vital role in tasks that demand dynamic decision-making, especially under high-risk and time-sensitive conditions. An excessive workload can lead to unexpected and disproportionate risks, whereas insufficient workload may cause disengagement, undermining task performance. This underscores the importance of maintaining an optimal level of mental focus in high-pressure situations to ensure successful task execution. This study leverages deep learning methods alongside functional connectivity measures to classify cognitive workload levels. Using the N-back EEG dataset, functional connectivity metrics such as Phase Locking Value (PLV), Phase Lagging Index (PLI), and Coherency are extracted after data pre-processing. These metrics, characterized as directed or non-directed, enable efficient computational analysis. A convolutional neural network (CNN) classifier is employed to categorize cognitive workload into three levels: low (0-back), medium (2-back), and high (3-back). The CNN-A architecture achieves peak performance with an accuracy of 93.75% using PLV, 87.5% using Coherency, and 68.75% using PLI.
Performance of Self-Compacting Concrete Mixed and Cured with Magnetized Water
This study investigates the effect of magnetized water (MW) on the mechanical and microstructural properties of self-compacting concrete (SCC). Four mixes with identical proportions were prepared to isolate the influence of MW at different stages: SCNTN (normal water used in both mixing and curing), SCNTM (normal water in mixing and MW in curing), SCMTN (MW in mixing and normal water in curing), and SCMTM (MW used in both mixing and curing). Compressive strength tests were conducted at 7 and 28 days, and microstructural characterization was performed using X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR). The results showed that using MW during mixing enhances early-age strength through improved dispersion and hydration of cement particles, while using MW during curing contributes more significantly to long-term hydration. The combined use of MW in both mixing and curing (SCMTM) achieved the highest strength values, with improvements of 25.8% at 7 days and 14.2% at 28 days compared to the control mix. Microstructural findings confirmed denser calcium–silicate–hydrate (C–S–H) gel formation and reduced unhydrated phases in MW-treated concrete. These results indicate that MW has a positive influence on both hydration and strength development in SCC.
The Effect of Lean Management on Innovation Performance: How Absorptive Capacity Affects Food Manufacturing Industry?
This study analyzes the effect of Lean Management Practices (LMP) on Innovation Performance (IP) through the mediating role of Absorptive Capacity (AC) in Indonesia’s food and beverage industry. A structured questionnaire adapted from validated instruments (Gaspersz, 2007; Cohen & Levinthal, 1990; Damanpour, 1991) was distributed to 200 managers and supervisors from food manufacturing firms listed in the Indonesian Ministry of Industry registry, yielding 180 valid responses (90% response rate). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0, assessing reliability, convergent, and discriminant validity (HTMT ≤ 0.90). The results indicate that Lean Management strongly influences Absorptive Capacity (β = 0.919, p < 0.001) and both directly and indirectly enhances Innovation Performance (β = 0.690 and 0.725, respectively). Effect sizes (f² = 0.482 – 5.431) and R² > 0.83 confirm the model’s high explanatory power, while confidence intervals (95%) validate the path significance. These findings demonstrate that lean implementation enhances innovation effectiveness primarily through knowledge assimilation and transformation. The study’s main limitation is its single-sector, self-reported cross-sectional design, which may introduce common-method bias. Future research should apply multi-method or longitudinal approaches to increase generalizability.
A Hybrid Deep-Learning and Evolutionary Feature-Selection Framework for Skin Lesion Classification: Application to Monkeypox Detection
The recent resurgence of Monkeypox has highlighted the urgent need for fast and accurate diagnostic tools. In this paper, we propose a new framework of hybrid deep learning to combine both DenseNet121 and MobileNetV2 to obtain both rich and supplementary attributes of the skin lesion images. By pooling the outputs of these two models in terms of features, we get the lightweight representation of the images as well as rich representations of the images. To improve the feature set, we use Genetic Algorithm (GA) which is useful in reducing the dimensions and eliminating redundancy. Optimized features are then categorized with the help of the Random Forest model, which has been selected due to its good performance and capacity to work with high-dimensional data. Using two publicly accessible datasets, MSID and MSLD, we tested our approach and obtained remarkable classification accuracies of 92.71% and 97.77%, respectively. These findings highlight the success of combining ensemble learning, evolutionary optimization, and deep learning to achieve accuracy and proper diagnosis of monkeypox through medical images.
Analysis and Design Android Augmented Reality Platform (Bilingual) for the Preservation of Cirebon Glass Paintings
This study analyzes and designs a bilingual Android Augmented Reality (AR) platform to support the digital preservation of Cirebon Glass Paintings. The development uses Unity and AR Core with a human-centered design approach. A total of 30 participants (n=30) evaluated usability and performance. 3D assets were produced using photogrammetry with an optimized polygon budget of ≤25,000 triangles per object. Model compression applied Draco and KTX2 to reduce memory load. Benchmark testing was conducted on Snapdragon 720G class devices. Experimental results show that the platform achieved a stable performance of ≥30 FPS (mean = 32.6 FPS) and low tracking error (RMSE = 1.8–2.3 cm) under indoor lighting. Usability testing yielded a mean System Usability Scale (SUS) score of 81.4 ± 6.2, indicating excellent user acceptance. Compared with existing AR heritage applications, this research provides a reproducible pipeline for AR-based cultural digitization with performance guarantees on mid-range smartphones. The findings imply that optimized AR asset workflows can enhance public interaction with intangible cultural heritage such as Cirebon Glass Paintings. Limitations include restricted device testing and the need for more complex ecotourism content integration in future development.
Unifying Dual-Pyramid Structure and Y–G Channel Synergy for Full-Reference Image Quality Assessment
Standard metrics such as SSIM often overlook complex chromatic distortions, creating a gap between objective scores and human judgment. To address this, we present the Synergistic Structural Similarity Index (SSSI), a metric grounded in a novel dual-pyramid strategy that integrates Gaussian-blurred stability with direct subsampling sharpness. Our method departs from luminance-only analysis by employing an equal, synergistic partnership between the luminance (Y) and Green (G) channels, mirroring the eye's spectral sensitivity. On the KADID-10k dataset, SSSI achieves an SROCC of 0.7793. This represents a significant 4% performance gain over the standard SSIM baseline, demonstrating that integrating chromatic data with dual-scale structural analysis provides a more accurate proxy for human visual perception.
Optimization of Image Compression Using K-Means Clustering for Digital Heritage Archives
Preserving digital cultural assets requires efficient compression to minimize storage and bandwidth costs. However, existing studies rarely evaluate K-Means Clustering on structurally complex objects such as the Prambanan Temple, leaving a research gap in assessing its performance against standard codecs. This study introduces a novel optimized K-Means pipeline with adaptive cluster selection and improved centroid initialization for compressing high-detail temple imagery. The method groups pixels based on color proximity, reducing redundancy while preserving key structural patterns. Experiments show that K-Means achieves PSNR 28.08–30.65 dB and SSIM 0.86–0.92, outperforming baseline JPEG at similar file sizes PSNR 26–28 dB, SSIM 0.80–0.87. This quantitative comparison demonstrates the model’s superior perceptual retention in textured stone regions. The methodological contribution lies in combining spatial–chromatic feature weighting with iterative centroid refinement, which increases cluster stability and reduces quantization artifacts. Findings confirm K-Means as a viable alternative for controlled-distortion compression. In conclusion, the proposed approach provides practical engineering implications, enabling reduced storage footprints, predictable reconstruction quality, and integration into hybrid compression pipelines for large-scale digital imaging systems.
Integrating Lean Warehousing and Systematic Analysis to Minimize Waste in Finished Product Storage: A Case Study and Generalizable Improvement Framework
Efficient warehouse management is critical for make-to-order production systems. This study investigates waste reduction in a metal casting company’s finished-product warehouse by applying Lean Warehousing principles. Using time-motion studies, Value Stream Mapping (VSM), Pareto diagrams, and fishbone diagrams, the research analyzed 13 workstations and identified five primary areas of waste. Simulation results demonstrated a significant cycle time reduction from 2,559 to 2,128 seconds, alongside an increased proportion of value-added activities. Although limited to a simulation, the findings highlight the effectiveness of Lean tools in standardizing work and redesigning layouts. The study concludes that continuous monitoring and Lean implementation are essential for achieving sustainable operational efficiency and reducing costs in industrial warehousing.
Flood Mitigation in East Coast Semarang City: Hydrological and Hydraulic Analysis for Polder System Optimization
Flooding is a recurring hazard across Indonesia, particularly in urban regions such as Semarang City, where high-intensity rainfall, tidal surges, land subsidence, and changes in land use contribute to frequent inundation. This study investigates the effectiveness of the Sringin Polder system in mitigating flood risks through an integrated hydrological and hydraulic analysis. The hydrological component involved calculating design rainfall values for various return periods, with a peak intensity of 113 mm recorded for the 10-year event. These values were used to simulate flood discharges using HEC-RAS.For the 25-year return period, the main river channel produced a simulated flood discharge of 47.42 m³/s, resulting in water overtopping the left embankment by 0.545 m at station P.30. calculated flood flow and the existing capacity of the river cross-sections.
SIMPORA: An Android-Based Data Management and Analytics Framework for Enhancing Community Sports Ecosystems
The increasing adoption of digital technologies in sports management highlights the need for efficient, data-driven systems that enhance community engagement and program evaluation. However, regional sports organizations in Indonesia, such as the Central Java Regional Indonesian Sports Community (KORMI), still face challenges in data integration, monitoring, and communication across stakeholders. This study aimed to design and evaluate SIMPORA, an Android-based data management and analytics system that supports sports activists, trainers, and administrators in planning, monitoring, and evaluating community sports programs. Using a Research and Development (R&D) approach with the Agile development model, the system was developed and tested among 45 users, including community sports leaders and KORMI administrators. Instruments included usability testing (System Usability Scale), task completion efficiency, and user satisfaction surveys. Quantitative evaluation results showed a mean usability score of 84 (excellent category), a 27% increase in data recording accuracy, and a 34% improvement in communication efficiency compared to manual reporting. User satisfaction reached 91%, indicating strong acceptance of the system. These findings demonstrate that SIMPORA effectively facilitates real-time data-driven decision-making and enhances coordination within community sports ecosystems. The system offers practical implications for expanding digital transformation initiatives in sports management across regional and national contexts.
Geopolymer Bio-Patch Repair: Microbially-Induced Calcite and Pozzolanic Fly Ash and Rice Husk Ash for Corrosion-Resistant of Reinforced Concrete Repair
Indonesia’s archipelagic environment subjects concrete structures to severe corrosion and sulfate attacks, necessitating sustainable repair solutions. This study evaluates a geopolymer-based bio-patch repair mortar utilizing fly ash (FA), rice husk ash (RHA), and Bacillus subtilis bacteria to induce Microbially Induced Calcite Precipitation (MICP). While the silica-rich ashes act as pozzolanic materials, the bacteria enhance durability by reducing oxygen levels and lowering corrosion rates. Microscopic analysis confirmed the formation of calcium carbonate ($CaCO_3$) and calcium silicate hydrate (C-S-H), both of which significantly improve mechanical properties. The research identified an optimum mixture of 93% FA, 6% RHA, and 1% bacteria, which achieved a compressive strength of 30 MPa, a density of 2.32 g/cm³, and a low water absorption rate of 3.25%. These results meet standard performance requirements, demonstrating that this eco-friendly geopolymer-MICP system is a viable and innovative contribution to structural repair in aggressive environments.
microbially induced calcite precipitation (MICP)
Importance–Performance Analysis of Bus Rapid Transit Service Attributes for Passenger Satisfaction and Sustainability
The Bus Rapid Transit (BRT) system in Bali serves as a vital component of sustainable urban mobility. This study uniquely integrates Importance-Performance Analysis (IPA) with technical recommendations to provide a comprehensive evaluation of BRT performance and its contribution to sustainable transportation. The analysis identifies key service strengths—such as seating comfort, air conditioning, cleanliness, and personnel service quality—that exceed passenger expectations. Conversely, deficiencies are evident in bus stop conditions, accessibility for disabled passengers, punctuality, and environmental sustainability. By linking IPA results with actionable technical strategies, the study recommends upgrading bus stop infrastructure, enhancing accessibility design, implementing real-time scheduling, and tracking systems, transitioning to eco-friendly bus fleets, and strengthening passenger information and security systems. This integrated approach not only highlights priority areas for improvement but also offers a practical roadmap for policymakers and transit authorities to enhance service quality, boost ridership, and advance Bali’s progress toward a resilient and sustainable urban transport system.
Implementation of The Green Campus Concept Based on The UI GreenMetric Guidelines Approach
Climate change and rapid urban development have increased the urgency of sustainable practices in higher education institutions. Green areas on campuses play a vital role in reducing carbon emissions, improving microclimate, and supporting ecological balance. This study analyzes the implementation of green area criteria based on the UI GreenMetric World University Rankings 2023 guidelines. A descriptive-analytical method was employed, combining field observations, documentation review, and interviews with campus facilities management. The Green Campus assessment results based on the UI GreenMetric guidelines, UMB is ranked 72nd among universities in Indonesia and 756th in the world based on the UI GreenMetric Ranking 2020. These results suggest that while the campus demonstrates strong compliance with the UI GreenMetric benchmarks, further improvement in conservation and community engagement is necessary.
Systemic Model of Driver Fatigue on Extreme Routes: PLS-SEM Analysis of Supervisor Support and Organizational Justice
Driver fatigue is a critical safety concern for long-distance bus operations, particularly on the extreme route of Bima–Mataram. The study examines the impact of supervisor support and perceived penalty fairness on drivers' compliance with rest periods and levels of fatigue. Data from 114 drivers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance–Performance Map Analysis (IPMA). The results indicate that supervisor support positively affects rest compliance (β = 0.38), which in turn decreases fatigue (β = –0.35); penalty fairness has a negative effect on fatigue (β = –0.29) directly. Accordingly, IPMA provides evidence that supervisor monitoring and penalty system consistency are high-impact yet underperforming priorities. These findings reveal that fatigue acts as a systemic variable developed by organizational and policy factors. The implications point out the necessity of improving supervisory capacity, penalty system reform to ensure fairness and transparency, and the integration of fatigue detection technologies to enhance safety interventions on high-risk routes.
Optimization of Off-Grid Solar Lighting Systems Using OEMOF Integrated with IoT Field Data — Case Study: Bukit Kunci, Indonesia
The growing need for efficient night lighting in natural tourist destinations highlights the importance of reliable and sustainable energy solutions. This study analyzes the optimization of solar-based lighting at Bukit Kunci, Indonesia, using the Open Energy Modelling Framework (OEMOF) combined with real-time monitoring via the IoT ThingSpeak platform. Photovoltaic (PV) panel data recorded at 15-second intervals during February–July 2025, yielding 532,520 records, were cleaned and aggregated as input to model the interaction of PV, batteries, LED lights, inverters, and backup generators, to minimize lifecycle cost and energy loss. Results indicate that the current PV capacity (0.4 kWp) supplies less than 50% of lighting demand, with a high levelized cost of energy (≈9.2 USD/kWh) and low reliability (self-sufficiency 3–22%). Optimization through capacity expansion (≈224 modules, ≈1.25 kWh storage) eliminated load loss probability and reduced LCOE to ≈0.05 USD/kWh. This approach demonstrates OEMOF’s potential to enhance system efficiency, ensure reliable night lighting, and support eco-tourism while offering replicability for rural destinations.
The Moderating Role of Digital Culture in the Relationship between Physical Ergonomics and Organizational Culture in SMI
This study investigates the moderating role of digital culture in the relationship between physical ergonomics, organizational culture, and the performance of Small and Medium Industries (SMI). Using survey data from 123 manufacturing SME employees in Tegal, Indonesia, the analysis employed Structural Equation Modeling (SEM-AMOS). The findings reveal that digital culture significantly moderates the relationship between physical ergonomics and organizational culture (β = 0.083; CR = 12.126; p < 0.001). However, physical ergonomics demonstrated an unexpected negative effect (β = –1.031; CR = –5.958; p < 0.001). In addition, organizational culture was found to have no significant influence on performance (β = 0.113; CR = 1.038; p = 0.299). These counterintuitive results highlight digital culture as a key moderator that strengthens the adaptive role of ergonomics. The study contributes by demonstrating the complex interplay between ergonomics, organizational culture, and digitalization, offering practical insights for SME managers to integrate ergonomic practices with digital initiatives to enhance competitiveness.
Development and Evaluation of an IndoBERT-Based NLP Model for Automated Clickbait Detection
The rapid growth of digital news platforms necessitates reliable and automated systems for maintaining content quality at scale. This study presents the engineering and evaluation of an IndoBERT-based Natural Language Processing (NLP) framework for automated clickbait detection in Indonesian news headlines. The proposed framework is designed as an end-to-end text classification pipeline, incorporating data preprocessing, tokenization, fine-tuning of a pretrained IndoBERT model, and systematic performance evaluation. Experiments were conducted using the CLICK-ID dataset comprising 15,000 Indonesian news headlines, with an 80:20 stratified train–test split. The fine-tuned model achieved an accuracy of 0.83, with a precision of 0.82, recall of 0.77, and an F1-score of 0.79 for the clickbait class. Further evaluation using threshold-independent metrics yielded a ROC-AUC value of 0.89 and an average precision of 0.88, indicating strong discriminative capability under moderate class imbalance. Comparative analysis shows that the proposed approach outperforms prior CNN, Bi-LSTM, and ensemble-based methods evaluated on the same dataset. These results demonstrate that IndoBERT provides a robust foundation for engineering automated clickbait detection systems tailored to Indonesian-language news streams.
Road Damage Management Model Due to Overloading: AHP Priorities and Policy Implications (Case Study of Lampung Province)
Lampung Province serves as a vital gateway for Trans-Sumatra-Java land transportation, placing a heavy burden on its 1,298 km of national roads. Managed by the Lampung National Road Traffic Management Agency (BPJN), these roads face accelerated degradation primarily due to Over-Dimension and Over-Loading (ODOL) vehicles. This study aims to identify causal variables of road damage, analyze significant parameters, and prioritize road sections for maintenance. Data were gathered through interviews and questionnaires with key stakeholders. Using the Analytical Hierarchy Process (AHP), the results identify ODOL vehicles as the primary cause of damage (67%), with the International Roughness Index (IRI) as the most critical parameter (27%). Consequently, the BTS Sukamaju – Km 10 Panjang Bandar Lampung segment is identified as the highest maintenance priority. The study recommends stricter ODOL enforcement through functional weighbridges, legal sanctions, and cross-agency oversight. These measures are essential to extend road service life, ensure safety, and improve regional transportation efficiency.
Optimization of Saccharomyces cerevisiae Dose in Eco-Enzyme Fermentation: Effects on pH, BOD, DO, Nitrite, and Nitrate
The contribution of Saccharomyces cerevisiae is expected to accelerate the fermentation process and enhance the microbial population involved in eco-enzyme production. This study aimed to analyze the potential role of S.cerevisiae in improving eco-enzyme production. A true experimental method was employed using a completely randomized design with four treatments and three replications. The results, based on the average matrix similarity test using the Wilks’ Lambda Test, showed that the significance values for pH, DO, BOD, nitrite, and nitrate were < 0.05. Therefore, H0 was rejected, indicating that varying doses of S.cerevisiae had a significant effect. Furthermore, significant differences were observed among the treatments for each dependent variable. In conclusion, S.cerevisiae contributes to enhancing the overall quality of eco-enzymes by facilitating fermentation and accelerating the decomposition of organic matter.
Physics-Informed Neural Network with Thevenin Equivalent Circuit for Accurate SOC Li-ion Battery Estimation
Accurate state of charge (SOC) estimation is essential for the safety, performance, and longevity of lithium-ion batteries. Physics-based models such as equivalent circuit models (ECMs) are computationally efficient but struggle under nonlinear and time-varying conditions, whereas purely data-driven approaches often lack interpretability. This study proposes a hybrid framework that integrates a physics-informed neural network (PINN) with a first-order Thevenin ECM for dynamic SOC estimation using only terminal voltage and current inputs. The method incorporates physically derived parameters including open-circuit voltage (OCV), polarization resistance, and capacitance identified through pulse testing. An eighth-order OCV–SOC polynomial regression optimized with a genetic algorithm (GA) enables nonlinear mapping, while the Newton–Raphson (NR) method is applied for final SOC estimation. Experimental validation was performed on 18 Ah lithium iron phosphate (LFP) cells over 300 charge–discharge cycles at 20 °C, extended up to 2000 cycles under 1C/2C rates with cut-off voltages of 3.7 V and 2.7 V. Comparative analysis with extended kalman filters (EKF) and standard neural networks (NN) demonstrates the superiority of the proposed method, achieving a root mean squared error (RMSE) of 0.103, mean absolute percentage error (MAPE) of 0.702%, and coefficient of determination (R²) of 0.998. By embedding physical constraints into the learning process, the PINN enhances accuracy, robustness, and generalizability, while reducing estimation uncertainty, thereby offering a scalable and interpretable solution for real-time battery management systems (BMS) in electric vehicles (EVs) and battery energy storage systems (BESS).
AI-Enabled CTAS and Digital Tax-Fraud Detection: A PLS-SEM Study in Indonesia
This study investigates the factors determining digital tax fraud based on the New Fraud Star Theory, with great emphasis on the moderating role of AI-empowered CTAS. Data were collected from 107 corporate taxpayers in Indonesia through a structured survey and analyzed using Partial Least Squares Structural Equation Modeling. The results indicated that System Pressure, Technological Capability, and External Digital Pressure significantly heightened fraud attempts, while Digital Opportunity, AI Rationalization, Cyber Arrogance, Internal IT Governance, and Techno-Culture were not significant. The model explained a substantial variance in the effectiveness of fraud detection with R² = 0.723. Moderation analysis showed that AI-powered CTAS significantly weakened the effects of System Pressure (X1×CTAS), Technological Capability (X4×CTAS), Internal IT Governance (X6×CTAS), and External Digital Pressure (X7×CTAS). These findings identify CTAS's strategic role in improving compliance by enabling real-time data integration, anomaly detection rules, and strengthened access control. Implications are that digital governance reforms should give full attention to the establishment of robust AI-empowered monitoring systems to minimize the risk of tax fraud effectively.
Adaptation and Validation of the Indonesian Version of R.I.G.H.T. Leadership Scale
Leadership has a significant impact on organizational resilience and employee well-being. In accordance with International Test Commission guidelines, this study translated and validated the R.I.G.H.T. Leadership Scale for Indonesian employees. Online information was gathered from 302 workers from various industries (162 men and 140 women, ages 19–41). Psychometric testing, expert review, and forward-backward translation were all part of the adaptation. With over 90% expert agreement, the content validity was excellent (I-CVI and S-CVI = 0.99). A good model fit was found by confirmatory factor analysis (χ² = 136.65, df = 80, p <.001; RMSEA = 0.036; CFI = 1.00; GFI = 0.94). With slight declines ascribed to cultural and linguistic factors, reliability was high (α = 0.804–0.884). Despite its limitations, which include young samples and a lack of test-retest data, the Indonesian version is generally valid, dependable, and helpful for evaluating leadership practices.
Empirical Analysis of the Impact of Labor Coefficients on Column Reinforcement Productivity in Construction Projects
Construction productivity, particularly in column reinforcement, is significantly influenced by labor as a key project component. Variations in labor coefficients determine efficiency in time, cost, and work quality, necessitating empirical analysis of their impact on productivity. This study examines the relationship between labor coefficients and column reinforcement productivity to improve construction project management efficiency. Using a quantitative approach with purposive sampling, 33 observation data were collected through field measurements and questionnaires from workers and foremen. Simple linear regression was applied to test labor coefficient significance, with results compared against PUPR Ministerial Regulation No. 8 of 2023 standards. Analysis revealed that field labor coefficients significantly affect column reinforcement productivity (p < 0.001), demonstrating that optimal labor utilization increases productivity. The comparison with ministerial standards evaluated field condition conformity with official provisions. The research hypothesis confirming significant influence between field labor coefficients and column reinforcement productivity was accepted, providing valuable insights for construction management practices.
Hybrid Expert System for Academic Stress Diagnosis Using Forward Chaining and Score Weighting
Academic stress classification is a significant challenge in education, as previous approaches often rely on opaque models or require large training datasets. This study develops a hybrid expert system for academic stress classification using forward chaining and Certainty Factor (CF) score fallback. The system was tested on 100 student cases with the following label distributions: Mild (48), Moderate (37), and High (13), classified independently by three experts. Label validity was tested using pairwise Cohen's kappa, yielding a mean value of 0.8280. The system achieved 100% accuracy, a 32% improvement over the classical forward chaining baseline (68%). Statistical evaluation using Wilson score intervals demonstrated high consistency across all key metrics (accuracy, precision, recall, F1-score) with a 95% CI of [96.4%, 100%]. The system is designed with an explicit and auditable rule structure, enabling deterministic classification based on symptoms. Although validation results are high, the unbalanced label distribution opens up the potential for spectrum bias. Going forward, the system is planned to be tested across institutions, assessed for integration with counseling services, and compared with other hybrid approaches.