Advance Sustainable Science Engineering and Technology
type: channel
description: 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.
publisher: Science and Technology Research Centre Universitas PGRI Semarang
language: en
publication name: Advance Sustainable Science Engineering and Technology
issn: 2715-4211
items: Uncertainty-Quantified Grid-Convergence Analysis of RANS Turbulence Models for 2-D Incompressible Backward-Facing Step Flow in OpenFOAM
items: Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features
items: Performance of Self-Compacting Concrete Mixed and Cured with Magnetized Water
items: A Hybrid Deep-Learning and Evolutionary Feature-Selection Framework for Skin Lesion Classification: Application to Monkeypox Detection
items: Analysis and Design Android Augmented Reality Platform (Bilingual) for the Preservation of Cirebon Glass Paintings
items: Optimization of Image Compression Using K-Means Clustering for Digital Heritage Archives
items: Integrating Lean Warehousing and Systematic Analysis to Minimize Waste in Finished Product Storage: A Case Study and Generalizable Improvement Framework
items: Flood Mitigation in East Coast Semarang City: Hydrological and Hydraulic Analysis for Polder System Optimization
items: SIMPORA: An Android-Based Data Management and Analytics Framework for Enhancing Community Sports Ecosystems
items: Importance–Performance Analysis of Bus Rapid Transit Service Attributes for Passenger Satisfaction and Sustainability
items: Implementation of The Green Campus Concept Based on The UI GreenMetric Guidelines Approach
items: Systemic Model of Driver Fatigue on Extreme Routes: PLS-SEM Analysis of Supervisor Support and Organizational Justice
items: The Moderating Role of Digital Culture in the Relationship between Physical Ergonomics and Organizational Culture in SMI
items: Development and Evaluation of an IndoBERT-Based NLP Model for Automated Clickbait Detection
items: Road Damage Management Model Due to Overloading: AHP Priorities and Policy Implications (Case Study of Lampung Province)
items: Optimization of Saccharomyces cerevisiae Dose in Eco-Enzyme Fermentation: Effects on pH, BOD, DO, Nitrite, and Nitrate
items: Physics-Informed Neural Network with Thevenin Equivalent Circuit for Accurate SOC Li-ion Battery Estimation
items: AI-Enabled CTAS and Digital Tax-Fraud Detection: A PLS-SEM Study in Indonesia
items: Adaptation and Validation of the Indonesian Version of R.I.G.H.T. Leadership Scale
items: Empirical Analysis of the Impact of Labor Coefficients on Column Reinforcement Productivity in Construction Projects
items: Hybrid Expert System for Academic Stress Diagnosis Using Forward Chaining and Score Weighting
items: ANN-Based Mechanical Property Prediction of Bio-Fibre for Wind Turbine Blade Materials with FEM Validation
items: Smart Hospitality and Innovation: The Role of Leadership, Local Wisdom, and Technology Integration in Five-Star Hotels
items: Development of A Parametric Cost Estimation Model for Landfill Construction Projects
items: Optimising Time Efficiency in Green Retrofit Jetty Projects through Envision and Lean-Based Value Stream Mapping
items: Engineering Assessment of Earthquake Resistant Building Code Based on Seismic Load Responses
items: Barium Removal from Produced Water Using RCC-Based Ceramic Adsorbent: Fixed-Bed Column Adsorption
items: “Demata 2.0”: An On-Device AI Assistive Technology for the Visually Impaired Integrating YOLOv10 and OCR
items: Developing a Trigonometry AR Book Using an Ethnomathematics Approach to Improve International Collage Students' Motivation and Learning Outcomes
items: Assensing the Impact of Advanced Driver Assistance Systems (ADAS) on Road Safety an Empirical Study Using Factor Analysis

Uncertainty-Quantified Grid-Convergence Analysis of RANS Turbulence Models for 2-D Incompressible Backward-Facing Step Flow in OpenFOAM
type: item
description: 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.
subject: Articles
  topic: section
subject: Backward‑Facing Step
  topic: keywords
subject: RANS Turbulence Models
  topic: keywords
subject: Grid Convergence Index (GCI)
  topic: keywords
subject: Uncertainty Quantification
  topic: keywords
subject: OpenFOAM Simulation
  topic: keywords
creator: Mickael Ruben Kaiway
creator: Joni
creator: Agustinus Giai
creator: Samuel Parlindungan Siregar
creator: Enos Tambing
creator: Obia Pius
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-23
publication date: 2025-12-23
starting page: 0260106
ending page: 0260106
doi: 10.26877/asset.v8i1.2390

Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features
type: item
description: 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.
subject: Articles
  topic: section
subject: Cognitive workload
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: CNN
  topic: keywords
subject: EEG
  topic: keywords
subject: Functional Connectivity
  topic: keywords
subject: Connectivity Metrics
  topic: keywords
creator: Vineeta Khemchandani
creator: Alok Singh Chauhan
creator: Shahnaz Fatima
creator: Jalauk Singh Maurya
creator: Abhay Singh Rathaur
creator: Narendra Kumar Sharma
creator: Daya Shankar Srivastava
creator: Vugar Abdullayev
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-22
publication date: 2026-01-22
starting page: 02601019
ending page: 02601019
doi: 10.26877/asset.v8i1.2833

Performance of Self-Compacting Concrete Mixed and Cured with Magnetized Water
type: item
description: 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.
subject: Articles
  topic: section
subject: self-compacting concrete
  topic: keywords
subject: magnetized water
  topic: keywords
subject: mixing
  topic: keywords
subject: curing process
  topic: keywords
subject: compressive strength
  topic: keywords
subject: XRD
  topic: keywords
subject: FTIR
  topic: keywords
creator: Majed Alshirah
creator: Mohd Zulham Affandi Bin Mohd Zahid
creator: Badorul Hisham Bin Abu Bakar
creator: Zul Fahmi Bin Mohamed Jaafar
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-23
publication date: 2025-12-23
starting page: 02601012
ending page: 02601012
doi: 10.26877/asset.v8i1.2825

A Hybrid Deep-Learning and Evolutionary Feature-Selection Framework for Skin Lesion Classification: Application to Monkeypox Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: Monkeypox Detection
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Genetic Algorithm
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Hybrid Neural Network
  topic: keywords
subject: Image classification
  topic: keywords
subject: Medical imaging
  topic: keywords
creator: Nidhi Chauhan
creator: Alok Singh Chauhan
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-21
publication date: 2026-01-21
starting page: 02601018
ending page: 02601018
doi: 10.26877/asset.v8i1.2786

Analysis and Design Android Augmented Reality Platform (Bilingual) for the Preservation of Cirebon Glass Paintings
type: item
description: 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.
subject: Articles
  topic: section
subject: Augmented Reality
  topic: keywords
subject: User-Centered Design (UCD)
  topic: keywords
subject: photogrammetry
  topic: keywords
subject: cultural heritage preservation
  topic: keywords
subject: mobile performance
  topic: keywords
creator: Suwandi Suwandi
creator: Willy Eka Septian
creator: Agus Sevtiana
creator: Noor Azura Binti Zakaria
creator: Vanya Adhelita
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-22
publication date: 2026-01-22
starting page: 02601022
ending page: 02601022
doi: 10.26877/asset.v8i1.2784

Optimization of Image Compression Using K-Means Clustering for Digital Heritage Archives
type: item
description: 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.
subject: Articles
  topic: section
subject: Image compression
  topic: keywords
subject: vector quantization
  topic: keywords
subject: PSNR
  topic: keywords
subject: SSIM
  topic: keywords
subject: digital archiving
  topic: keywords
creator: Yoga Sahria
creator: Putu Sudira
creator: Priyanto
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2026-01-05
publication date: 2026-01-05
starting page: 02601020
ending page: 02601020
doi: 10.26877/asset.v8i1.2772

Integrating Lean Warehousing and Systematic Analysis to Minimize Waste in Finished Product Storage: A Case Study and Generalizable Improvement Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: lean warehousing
  topic: keywords
subject: make-to-order (MTO)
  topic: keywords
subject: process cycle efficiency
  topic: keywords
subject: reduce waste generation
  topic: keywords
subject: time motion study
  topic: keywords
subject: VSM
  topic: keywords
creator: Qurtubi
creator: Haswika
creator: Hari Purnomo
creator: Pham Trung Kien
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2026-01-19
publication date: 2026-01-19
starting page: 02601011
ending page: 02601011
doi: 10.26877/asset.v8i1.2765

Flood Mitigation in East Coast Semarang City: Hydrological and Hydraulic Analysis for Polder System Optimization
type: item
description: 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.
subject: Articles
  topic: section
subject: Bankfull Capacity
  topic: keywords
subject: Flood mitigation
  topic: keywords
subject: HEC-RAS simulation
  topic: keywords
subject: Nakayasu
  topic: keywords
subject: Sringin Watershed
  topic: keywords
creator: Ikhwanudin
creator: Imadudin Harjanto
creator: Risdiana Cholifatul Afifah
creator: Farida Yudaningrum
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2026-01-13
publication date: 2026-01-13
starting page: 02601013
ending page: 02601013
doi: 10.26877/asset.v8i1.2764

SIMPORA: An Android-Based Data Management and Analytics Framework for Enhancing Community Sports Ecosystems
type: item
description: 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.
subject: Articles
  topic: section
subject: system management
  topic: keywords
subject: coaching sport
  topic: keywords
subject: android
  topic: keywords
subject: data interpretation
  topic: keywords
subject: technology sport
  topic: keywords
creator: Edi Purwanto
creator: Heny Setyawati
creator: Cahyo Yuwono
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-22
publication date: 2026-01-22
starting page: 02601025
ending page: 02601025
doi: 10.26877/asset.v8i1.2735

Importance–Performance Analysis of Bus Rapid Transit Service Attributes for Passenger Satisfaction and Sustainability
type: item
description: 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.
subject: Articles
  topic: section
subject: Bus Rapid Transit (BRT)
  topic: keywords
subject: Importance–Performance Analysis (IPA)
  topic: keywords
subject: service quality modeling
  topic: keywords
subject: public transport engineering
  topic: keywords
subject: performance gap analysis
  topic: keywords
creator: Putu Hermawati
creator: Nadzirah Hj. Zainordin
creator: Thung Wei Eng
creator: A. A. Ayu Asta Oktaviani Putri
creator: I Ketut Sutapa
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2026-01-06
publication date: 2026-01-06
starting page: 0260107
ending page: 0260107
doi: 10.26877/asset.v8i1.2725

Implementation of The Green Campus Concept Based on The UI GreenMetric Guidelines Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: biodiversity
  topic: keywords
subject: green campus
  topic: keywords
subject: green open space
  topic: keywords
subject: sustainability
  topic: keywords
subject: UI GreenMetric
  topic: keywords
creator: Albert Eddy Husin
creator: Bernadette Detty Kussumardianadewi
creator: Hanky Guruh
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-30
publication date: 2025-10-30
starting page: 02504025
ending page: 02504025
doi: 10.26877/asset.v7i4.2721

Systemic Model of Driver Fatigue on Extreme Routes: PLS-SEM Analysis of Supervisor Support and Organizational Justice
type: item
description: 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.
subject: Articles
  topic: section
subject: Driver fatigue
  topic: keywords
subject: Extreme routes
  topic: keywords
subject: Long-distance buses
  topic: keywords
subject: Organizational justice
  topic: keywords
subject: Supervisor support
  topic: keywords
creator: Ahmad
creator: Leksmono Suryo Putranto
creator: Dadang Mohamad
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504032
ending page: 02504032
doi: 10.26877/asset.v7i4.2681

The Moderating Role of Digital Culture in the Relationship between Physical Ergonomics and Organizational Culture in SMI
type: item
description: 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.
subject: Articles
  topic: section
subject: digital culture
  topic: keywords
subject: physical ergonomics
  topic: keywords
subject: organizational culture
  topic: keywords
subject: performance
  topic: keywords
subject: small and medium industries
  topic: keywords
creator: Mohammad Fajar Nurwildani
creator: Hari Purnomo
creator: Elisa Kusrini
creator: Hartomo Soewardi
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-23
publication date: 2025-12-23
starting page: 0260110
ending page: 0260110
doi: 10.26877/asset.v8i1.2666

Development and Evaluation of an IndoBERT-Based NLP Model for Automated Clickbait Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: IndoBERT
  topic: keywords
subject: NLP system design
  topic: keywords
subject: clickbait detection
  topic: keywords
subject: machine learning pipeline
  topic: keywords
subject: model evaluation
  topic: keywords
creator: Sandy Kurniawan
creator: Adhe Setya Pramayoga
creator: Yeva Fadhilah Ashari
creator: Muhammad Afrizal Amrustian
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-22
publication date: 2026-01-22
starting page: 02601021
ending page: 02601021
doi: 10.26877/asset.v8i1.2637

Road Damage Management Model Due to Overloading: AHP Priorities and Policy Implications (Case Study of Lampung Province)
type: item
description: 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.
subject: Articles
  topic: section
subject: Overdimension/Overloading (ODOL)
  topic: keywords
subject: Analytic Hierarchy Process (AHP)
  topic: keywords
subject: International Roughness Index (IRI)
  topic: keywords
subject: Road Pavement Priority
  topic: keywords
subject: Lampung Province
  topic: keywords
subject: Road Asset Management
  topic: keywords
creator: M. Enriko Tosulpa
creator: Leksmono Suryo Putranto
creator: Rahayu Sulistyorini
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-31
publication date: 2025-12-31
starting page: 02601015
ending page: 02601015
doi: 10.26877/asset.v8i1.2629

Optimization of Saccharomyces cerevisiae Dose in Eco-Enzyme Fermentation: Effects on pH, BOD, DO, Nitrite, and Nitrate
type: item
description: 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.
subject: Articles
  topic: section
subject: BOD
  topic: keywords
subject: eco-enzyme
  topic: keywords
subject: fermentation
  topic: keywords
subject: Saccharomyces cerevisiae
  topic: keywords
subject: wastewater valorization
  topic: keywords
creator: Endah Rita Sulistya Dewi
creator: Maria Ulfah
creator: Ary Susatyo Nugroho
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-28
publication date: 2025-10-28
starting page: 02504028
ending page: 02504028
doi: 10.26877/asset.v7i4.2626

Physics-Informed Neural Network with Thevenin Equivalent Circuit for Accurate SOC Li-ion Battery Estimation
type: item
description: 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).
subject: Articles
  topic: section
subject: battery management systems (BMS)
  topic: keywords
subject: energy storage systems (ESS)
  topic: keywords
subject: equivalent circuit model (ECM)
  topic: keywords
subject: hybrid modeling
  topic: keywords
subject: state of charge (SOC)
  topic: keywords
subject: uncertainty quantification
  topic: keywords
creator: Chico Hermanu Brillianto Apribowo
creator: Muhamad Dzaky Ashidqi
creator: Zainal Arifin
creator: Henry Probo Santoso
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-09-25
publication date: 2025-09-25
starting page: 02504023
ending page: 02504023
doi: 10.26877/asset.v7i4.2613

AI-Enabled CTAS and Digital Tax-Fraud Detection: A PLS-SEM Study in Indonesia
type: item
description: 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.
subject: Articles
  topic: section
subject: anomaly detection
  topic: keywords
subject: ctas
  topic: keywords
subject: digital governance
  topic: keywords
subject: fraud detection
  topic: keywords
subject: tax administration
  topic: keywords
creator: Alif Faruqi Febri Yanto
creator: Nuraini Sari
creator: Defrina Eka Orchidta Ramadina
creator: Tomy Prasetia
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504033
ending page: 02504033
doi: 10.26877/asset.v7i4.2609

Adaptation and Validation of the Indonesian Version of R.I.G.H.T. Leadership Scale
type: item
description: 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.
subject: Articles
  topic: section
subject: health leadership
  topic: keywords
subject: indonesian workers
  topic: keywords
subject: scale adaptation
  topic: keywords
subject: test validation
  topic: keywords
creator: Muksidah Pratiwi
creator: Zahrotur Rusyda Hinduan
creator: Fitriani Yustikasari Lubis
creator: Cherrly April
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504035
ending page: 02504035
doi: 10.26877/asset.v7i4.2600

Empirical Analysis of the Impact of Labor Coefficients on Column Reinforcement Productivity in Construction Projects
type: item
description: 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.
subject: Articles
  topic: section
subject: Column Reinforcement Productivity
  topic: keywords
subject: Construction Projects
  topic: keywords
subject: Labor Coefficient
  topic: keywords
creator: Darmawan Pontan
creator: Pentagon Chen
creator: Daniel Mundung
creator: Manisha Rucitawangi
creator: Indrawati Sumeru
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504040
ending page: 02504040
doi: 10.26877/asset.v7i4.2590

Hybrid Expert System for Academic Stress Diagnosis Using Forward Chaining and Score Weighting
type: item
description: 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.
subject: Articles
  topic: section
subject: cademic stress
  topic: keywords
subject: diagnosis
  topic: keywords
subject: expert system
  topic: keywords
subject: forward chaining
  topic: keywords
subject: score weighting
  topic: keywords
creator: Indra Gunawan
creator: Adhika Pramita Widyassari
creator: Ismail Yusuf Panessai
creator: Jonathan Rante Carreon
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504036
ending page: 02504036
doi: 10.26877/asset.v7i4.2586

ANN-Based Mechanical Property Prediction of Bio-Fibre for Wind Turbine Blade Materials with FEM Validation
type: item
description: The increasing demand for renewable energy highlights the need for sustainable materials in wind turbine blade design. Conventional fiberglass blades, while effective, present environmental and disposal challenges, motivating the exploration of bio-composites as greener alternatives. This study aims to develop and validate an integrated framework that combines experimental validation, Finite Element Method (FEM) pre-screening, Artificial Neural Networks (ANN), and Rule of Mixtures (RoM) validation to evaluate the feasibility of bio-fibre wind turbine blades Mechanical properties of flax, hemp, sisal, jute, pineapple fiber, and resin are obtained from previously published experimental studies available in the literature, with resin content fixed at 90% and permutations generated for ANN training. Experimental tensile testing on a 90% resin–10% pineapple fiber composite yields 131 MPa, closely matching the permutation prediction of 118.6 MPa, confirming dataset reliability. FEM simulations are then employed to pre-screen potential maximum performance values within the dataset range, ensuring the physical feasibility of ANN input properties. Using these validated inputs, the ANN predicts feasible bio-composite compositions, which are further compared against RoM estimations. The results show that ANN predictions remain within a 7% deviation from RoM values, demonstrating consistency with micromechanical theory. This integrated framework highlights that FEM-based input screening enhances ANN prediction reliability, and pineapple-based bio-composites can serve as sustainable and technically viable alternatives for wind turbine blade applications.
subject: Articles
  topic: section
subject: Artificial Neural Network (ANN)
  topic: keywords
subject: Finite Element Method (FEM)
  topic: keywords
subject: Bio-Composites
  topic: keywords
subject: Pineapple Fiber
  topic: keywords
subject: Wind Turbine Blades
  topic: keywords
creator: Siaga Whiky Setia
creator: Nu Rhahida Arini
creator: Bima Sena Bayu Dewantara
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-23
publication date: 2025-12-23
starting page: 0260109
ending page: 0260109
doi: 10.26877/asset.v8i1.2482

Smart Hospitality and Innovation: The Role of Leadership, Local Wisdom, and Technology Integration in Five-Star Hotels
type: item
description: Innovative work behavior is a key driver of technology-based service innovation in the hospitality industry. This study integrates innovative work behavior into Technology-Enabled Innovative Behavior and reconceptualizes organizational commitment into Technology Adoption Commitment to capture employees' psychological attachment to technology adoption underexplored area. Commitment to capture employees’ psychological attachment to adopting technologies. This study aims to examine and analyze the influence of transformational leadership on Technology-Enabled Innovative Behavior, and through the mediating roles of paras-paros citizenship behavior and Technology Adoption Commitment. Using survey data from 377 employees of five-star hotels in Bali analyzed with PLS-SEM, the results show that transformational leadership significantly enhances Technology-Enabled Innovative Behavior, with Paras Paros Citizenship Behavior as a partial mediator, while Technology Adoption Commitment has no mediating effect. Higher Technology-Enabled Innovative Behavior is reflected in the frequent use of Property Management Systems, digital payment, and mobile check-in, whereas lower use of AI-driven customer service indicates areas for further innovation. The findings highlight that leadership and Paras Paros Citizenship Behavior provide strong cultural support, but innovation outcomes depend on the accessibility and integration of specific technologies. Strengthening leadership practices, local wisdom values, and the operational integration of underutilized tools can accelerate digital transformation in the hospitality industry.
subject: Articles
  topic: section
subject: Transformational Leadership
  topic: keywords
subject: Paras Paros Citizenship Behavior
  topic: keywords
subject: Technology Adoption Commitment
  topic: keywords
subject: Technology-Enabled Innovative Behavior
  topic: keywords
subject: Smart Hospitality
  topic: keywords
creator: Ni Nyoman Suliati
creator: Noermijati
creator: Achmad Sudiro
creator: Desi Tri Kurniawati
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-14
publication date: 2025-10-14
starting page: 02504020
ending page: 02504020
doi: 10.26877/asset.v7i4.2462

Development of A Parametric Cost Estimation Model for Landfill Construction Projects
type: item
description: Indonesia’s waste management system is still dominated by the collect–transport–dispose approach, making landfills crucial for environmental sustainability. Landfill construction is a complex process requiring accurate cost estimation to prevent overruns and delays. This study develops a landfill construction cost estimation model using the Cost Significant Model (CSM) approach. Data were obtained from landfill project budgets in Java, Indonesia (2013–2021) and analyzed using multiple linear regression in SPSS. Results show that landfill block, leachate treatment installation, and operational road are the most significant cost components, the estimation model is Y = 3698103502.04 + 1.301X3 + 0.371X4 + 1.236X5. The model falls within the class 4 cost estimation accuracy range according to AACE International standards, making it suitable for feasibility study use. This study introduces a model, context-specific cost estimation model for landfill projects in Indonesia, supporting effective planning and sustainable infrastructure development
subject: Articles
  topic: section
subject: cost estimation
  topic: keywords
subject: landfill construction
  topic: keywords
subject: cost significant model
  topic: keywords
creator: Nurhayati Junaedi
creator: Ridho Bayuaji
creator: Alfred Jonathan Susilo
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-23
publication date: 2025-10-23
starting page: 02504030
ending page: 02504030
doi: 10.26877/asset.v7i4.2424

Optimising Time Efficiency in Green Retrofit Jetty Projects through Envision and Lean-Based Value Stream Mapping
type: item
description: Indonesia, as a strategic global maritime axis, has only 7.03% of its islands equipped with jetties, and jetty construction poses environmental challenges due to emissions and ecological impacts. Green retrofitting provides a sustainable solution by improving energy efficiency in existing jetties. The Envision rating system guides the transition from conventional to green infrastructure, assessing quality of life, leadership, resource allocation, natural environment, and climate resilience. Despite its benefits, 32% of green projects experience delays. This study analyzes the key factors influencing time performance optimization in Green Retrofit Jetty projects using Lean–Value Stream Mapping (VSM). Using SEM-PLS, ten critical factors were identified. Lean–VSM facilitates process visualization and waste elimination. The Green Retrofit Jetty, achieving an Envision Platinum rating, reduced project duration from 250 to 220 days, demonstrating a 12% improvement in time performance while supporting efficient and sustainable jetty development.
subject: Articles
  topic: section
subject: Envision
  topic: keywords
subject: Green Retrofit
  topic: keywords
subject: Jetty
  topic: keywords
subject: Time Optimisation
  topic: keywords
subject: Lean Construction
  topic: keywords
subject: SEM-PLS
  topic: keywords
subject: Value Stream Mapping
  topic: keywords
creator: Aprilian Ismana
creator: Mawardi Amin
creator: Pio Ranap Tua Naibaho
creator: Deprizon Syamsunur
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2026-01-13
publication date: 2026-01-13
starting page: 02601016
ending page: 02601016
doi: 10.26877/asset.v8i1.2394

Engineering Assessment of Earthquake Resistant Building Code Based on Seismic Load Responses
type: item
description: Earthquake-resistant building design is fundamentally aimed at safeguarding life safety while controlling structural damage and preserving post-earthquake functionality under uncertain seismic actions. Although contemporary seismic codes provide detailed procedures for estimating earthquake-induced loads, differences in seismic hazard representation, force distribution rules, and deformation assumptions can lead to considerable variation in predicted structural response. This study presents a comprehensive engineering assessment of earthquake-resistant building codes based on seismic load responses in reinforced concrete moment-resisting frame structures. An integrated analytical framework combining elastic seismic analysis and nonlinear static performance evaluation is adopted to examine global force demand, displacement behavior, stiffness degradation, and post-yield response. Particular attention is given to the interaction between force-based seismic demand indicators, such as base shear and story forces, and deformation-based performance measures, including interstory drift and performance point characteristics. By systematically evaluating structural response across elastic and inelastic stages, the study demonstrates that reliance on elastic force demand alone is insufficient for capturing true seismic performance. The results emphasize the importance of performance-oriented assessment in enhancing the reliability, consistency, and resilience of earthquake-resistant building design.
subject: Articles
  topic: section
subject: disaster risk reduction
  topic: keywords
subject: earthquake-resistant design
  topic: keywords
subject: nonlinear static analysis
  topic: keywords
subject: performance-based assessment
  topic: keywords
subject: seismic load responses
  topic: keywords
subject: structural resilience
  topic: keywords
creator: Dwi Yanto
creator: Tavio
creator: Andrew Hartanto Jusuf
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-23
publication date: 2025-12-23
starting page: 0260102
ending page: 0260102
doi: 10.26877/asset.v8i1.2400

Barium Removal from Produced Water Using RCC-Based Ceramic Adsorbent: Fixed-Bed Column Adsorption
type: item
description: This study focuses on the removal of barium ions (Ba²⁺) from produced water, a common challenge in industrial wastewater treatment due to barium’s toxicity and scaling potential. To address this, the research introduces a novel ceramic composite adsorbent formulated from natural clay and residue catalytic cracking (RCC) spent catalyst, combining low cost, sustainability, and enhanced adsorption performance. The main objective is to evaluate the adsorption efficiency of this composite in a fixed-bed column system under varying operational conditions, while also modeling its dynamic behavior. Produced water with an initial barium concentration of 0.58 mg/L (pH 8.8) was fed in up-flow mode at flow rates of 6, 7, and 8 mL/min using a peristaltic pump. Effluent samples collected over 180 minutes were analyzed by UV- Vis. spectrophotometry. Results showed that lower flow rates increased contact time and improved adsorption efficiency, with breakthrough delayed to ~210 minutes at 6 mL/min compared to 160 minutes at 8 mL/min. Breakthrough modeling indicated that the Thomas model best represented the data (R² ≥ 0.95), while the Yoon–Nelson model reliably predicted 50% breakthrough time. This work demonstrates that clay–RCC ceramic composites are effective, low-cost, and sustainable adsorbents.
subject: Articles
  topic: section
subject: barium removal
  topic: keywords
subject: fixed-bed adsorption
  topic: keywords
subject: ceramic adsorbent
  topic: keywords
subject: produced water
  topic: keywords
subject: breakthrough curve
  topic: keywords
creator: Netty Herawati
creator: Subriyer Nasir
creator: Kiagus Ahmad Roni
creator: Muhammad Arif Karim
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504029
ending page: 02504029
doi: 10.26877/asset.v7i4.2399

“Demata 2.0”: An On-Device AI Assistive Technology for the Visually Impaired Integrating YOLOv10 and OCR
type: item
description: Accessibility to printed materials and independent recognition of the environment remain key challenges for students with visual impairments. To address this issue, this study introduces Demata 2.0, a fully offline on device multimodal AI system. The system integrates Google ML Kit for Optical Character Recognition (OCR) and the YOLOv10 model via TensorFlow Lite for object detection. A mathematical distance algorithm in the RGB color space enables color identification. Evaluation showed that object detection achieved a mean average precision of 31.83%, with an average processing speed of 2–3 FPS. For OCR, the system recorded a Character Error Rate (CER) of 4.81% and a Word Error Rate (WER) of 10.71% on printed documents. The RGB algorithm also determined the closest possible color effectively. Overall, Demata 2.0 advances assistive technology by providing an efficient and practical blueprint for AI integration.
subject: Articles
  topic: section
subject: assistive technology
  topic: keywords
subject: on-device AI
  topic: keywords
subject: optical character recognition
  topic: keywords
subject: visual impairment
  topic: keywords
subject: YOLOv10
  topic: keywords
creator: Reza Febri Abadi
creator: Toni Yudha Pratama
creator: Neti Asmiati
creator: Ade Anggraini Kartika Devi
creator: Joko Yuwono
creator: Dwi Setia Permana
creator: Febrian Alwan Bahrudin
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-28
publication date: 2025-10-28
starting page: 02504026
ending page: 02504026
doi: 10.26877/asset.v7i4.2380

Developing a Trigonometry AR Book Using an Ethnomathematics Approach to Improve International Collage Students' Motivation and Learning Outcomes
type: item
description: This study presents the development of an Augmented Reality (AR)-based trigonometry learning media integrating ethnomathematics to enhance students’ conceptual understanding and engagement. The AR system was designed using the ADDIE framework, emphasizing the design and development stages, with validation conducted through expert review and limited user trials at Universitas PGRI Semarang (UPGRIS) and Universiti Teknologi Malaysia (UTM). The AR Book integrates local cultural geometries such as traditional architectural patterns and ornaments into interactive 3D visualizations to contextualize trigonometric concepts. The system was developed using Unity 3D and Vuforia SDK, optimized for Android devices. Validation results indicated high content validity (92%) and strong media practicality (88%), while small-scale implementation showed improved student motivation and learning outcomes. Beyond educational outcomes, the research contributes to the advancement of culturally adaptive AR learning media design and demonstrates the potential scalability of ethnomathematics-based AR systems in STEM education.
subject: Articles
  topic: section
subject: trigonometry augmented reality systems
  topic: keywords
subject: ethnomathematics integration
  topic: keywords
subject: trigonometry media design
  topic: keywords
subject: educational technology
  topic: keywords
subject: interactive learning tools
  topic: keywords
creator: Achmad Buchori
creator: Farida Nursyahidah
creator: Dina Prasetyowati
creator: Sharifah Osman
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-26
publication date: 2025-12-26
starting page: 02601014
ending page: 02601014
doi: 10.26877/asset.v8i1.2372

Assensing the Impact of Advanced Driver Assistance Systems (ADAS) on Road Safety an Empirical Study Using Factor Analysis
type: item
description: Road traffic accidents remain a critical global concern, especially in low- and middle-income countries. Advanced Driver Assistance Systems (ADAS) are introduced as proactive safety technologies in the industry 4.0 era. This study aims to assess the impact of ADAS on road safety through an empirical approach. A quantitative survey involving 260 licensed drivers was conducted, followed by qualitative interviews to provide contextual insights. The dataset was confirmed reliable (KMO = 0.82; Cronbach’s α = 0.87), and factor analysis identified four latent constructs: Collision Avoidance, Driver Behavior and Acceptance, System Reliability, and Road Safety Impact, explaining 68.5% of variance. Results indicate that collision avoidance and system reliability are the strongest predictors of user trust, while road safety impact emerges as an independent factor emphasizing societal benefits. The findings highlight that ADAS adoption should be framed not only as technological acceptance but also as a contribution to sustainable mobility and SDG 3.
subject: Articles
  topic: section
subject: ADAS
  topic: keywords
subject: factor analysis
  topic: keywords
subject: industry 4.0
  topic: keywords
subject: road safety
  topic: keywords
subject: sensor integration
  topic: keywords
creator: Anang Pramono
creator: Eko Abdul Goffar
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504038
ending page: 02504038
doi: 10.26877/asset.v7i4.2371