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: Performance of Self-Compacting Concrete Mixed and Cured with Magnetized Water
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: 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: 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
items: Quantifying the Causal Impact of Employment Trends on Academic Performance Using Time-Series and Public Interest Data in Indonesia
items: Influence of Mixing Time on the Hardness and Structure of Local Clay-Based Crucibles
items: Evaluating Ordinal Multivariate Models under Multicollinearity via Pairwise Likelihood: A Simulation Perspective
items: High-Resolution Smart Card-Based OD Matrix for Optimizing Jakarta’s LRT Operations
items: Predicting Habitat Suitability of Mahseer Fish (Tor spp.) in Tropical River Systems Using MaxEnt and Google Earth Engine: A Geospatial Modeling Approach
items: Biosorption of Chromiun in Batik Wastewater Using SCOBY Microbial Biomass: A Sustainable Bioremediation Approach
items: Development of an IoT-based Soil Nutrient Monitoring and GIS Mapping System for Precision Agriculture
items: Determinants of MSME Sustainability: A Regression-Based Study in Labuhanbatu
items: Prediction of Soil Nutrients from Different Soil Textures using Portable Spectrometer and Machine Learning
items: Comparative Analysis of Observed and Empirical Rainfall Distribution for Flood Hydrograph Modeling

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

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

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

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

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

Quantifying the Causal Impact of Employment Trends on Academic Performance Using Time-Series and Public Interest Data in Indonesia
type: item
description: This study quantifies the causal impact of employment trends on academic performance using a hybrid model of survey data and time-series public interest data from Google Trends in Indonesia. Employing Granger causality and regression analysis, the research investigates eight determinants of GPA and their relationship to labor indicators. A purposive sample of 40 respondents and secondary data from 2011–2019 were analyzed. Granger tests reveal significant one-way causality from employment to GPA indicators, particularly in parental monitoring (F = 7.06; p < 0.05) and learning motivation (F = 9.68; p < 0.05). Regression analysis supports these findings with R² values above 0.50. Results highlight the potential of integrating behavioral data into educational analytics. This research contributes methodological innovation by incorporating public interest data to explain academic outcomes, with implications for predictive modeling in education policy and planning.
subject: Articles
  topic: section
subject: Causal Inference
  topic: keywords
subject: CGPA
  topic: keywords
subject: Google Trends
  topic: keywords
subject: Granger Causality
  topic: keywords
subject: Time-Series Analysis
  topic: keywords
creator: Alif Noorachmad Muttaqin
creator: Muharman Lubis
creator: Tomi Mulhartono
creator: Arif Ridho Lubis
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-08-28
publication date: 2025-08-28
starting page: 0250409
ending page: 0250409
doi: 10.26877/asset.v7i4.2358

Influence of Mixing Time on the Hardness and Structure of Local Clay-Based Crucibles
type: item
description: Although clay crucibles are frequently utilized in regional industries, their inadequate mechanical strength often causes durability issues. This study investigates the influence of mixing duration on the Vickers hardness and macrostructure of crucibles composed of local clay, kaolin, and molasses. The composition was made up of 47.5% clay, 47.5% kaolin, and 5% molasses as a binder, with 15% water added relative to the total weight. Durations of 15, 30, and 45 minutes were evaluated to determine their impact on material qualities. The findings indicated a positive relationship between mixing duration and hardness. At 15 minutes, the mean hardness was 4.1 HV, which escalated to 8.5 HV at 30 minutes and 12.4 HV at 45 minutes. The increased hardness with extended mixing durations indicates a more homogeneous particle dispersion and enhanced bonding among the raw ingredients. The findings suggest that increasing the mixing time can elevate the quality and longevity of locally manufactured crucibles, rendering them more appropriate for small-scale metallurgical applications.
subject: Articles
  topic: section
subject: crucible
  topic: keywords
subject: local clay
  topic: keywords
subject: kaolin
  topic: keywords
subject: mixing time
  topic: keywords
subject: binder
  topic: keywords
subject: refractory material
  topic: keywords
subject: Vickers hardness
  topic: keywords
creator: Rusiyanto
creator: Rifky Ismail
creator: Athanasius Priharyoto Bayuseno
creator: Daffa Agya Mahardika
creator: Deni Fajar Fitriyana
creator: Wirawan Sumbodo
creator: Aldias Bahatmaka
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-23
publication date: 2025-12-23
starting page: 0260101
ending page: 0260101
doi: 10.26877/asset.v8i1.2314

Evaluating Ordinal Multivariate Models under Multicollinearity via Pairwise Likelihood: A Simulation Perspective
type: item
description: This study examines the effect of multicollinearity on ordinal regression through a two-stage Monte Carlo simulation. A synthetic population of 2,000,000 observations was generated with predictors drawn from a normal distribution, and responses simulated using an ordinal probit model. A Monte Carlo procedure was employed with 10 repetitions, each consisting of 100 random samples of 1,000 observations. Parameter estimation employed Maximum Likelihood Estimation (MLE) for univariate models and Pairwise Likelihood (PL) for multivariate models, with performance assessed using mean squared error (MSE), bias, and computation time. Results show that multicollinearity had negligible impact on estimator bias and MSE, confirming the robustness of both MLE and PL to correlated predictors. However, severe multicollinearity substantially increased computation time, indicating a trade-off between estimator stability and efficiency. These findings highlight PL as a viable approach for analyzing complex ordinal data, particularly in applications such as socio-economic surveys and health metrics where predictor correlation is unavoidable.
subject: Articles
  topic: section
subject: Latent Variable Modeling
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
subject: Multivariate Ordinal Regression
  topic: keywords
subject: Ordinal Probit
  topic: keywords
subject: Pairwise Likelihood Estimation (PL)
  topic: keywords
creator: Achmad Fauzan
creator: Kusman Sadik
creator: Anang Kurnia
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-28
publication date: 2025-10-28
starting page: 02504024
ending page: 02504024
doi: 10.26877/asset.v7i4.2282

High-Resolution Smart Card-Based OD Matrix for Optimizing Jakarta’s LRT Operations
type: item
description: Efficient urban mobility is essential to support transportation planning and policy. However, traditional methods are often limited in data resolution, lacking the ability to describe passenger movement dynamics in detail. This study aims to analyze passenger mobility patterns using high-resolution tap-in/tap-out data from the closed-loop LRT system in Jakarta during January-February 2025. The methods used include constructing an origin-destination (OD) matrix based on 185,512 trip records, as well as temporal and spatial analysis of passenger flows. The results showed the existence of peak hour patterns on weekdays (07.00-09.00 and 17.00-19.00), trip spikes on weekends and holidays (14.00-18.00), and high flow concentrations at interchange stations such as Velodrome and North Boulevard. While data from the closed system allows for accurate trip tracking, potential data gaps due to technical errors or user behavior remain a concern for long-term analysis. The findings suggest that high-resolution smart card data can provide operationally relevant insights for short-term decision-making, such as schedule adjustments or fleet allocation. However, for long-term strategic planning, integration with predictive models and other planning tools remains necessary. This research fills a gap in the literature by showing that even limited-duration datasets can be leveraged to effectively support data-driven transportation management.
subject: Articles
  topic: section
subject: Light Rail Transit (LRT)
  topic: keywords
subject: smart card
  topic: keywords
subject: transit analysis
  topic: keywords
subject: LRT performance metrics
  topic: keywords
subject: transit demand estimation
  topic: keywords
subject: urban mobility
  topic: keywords
creator: Ikhsan Rahmat Fadillah
creator: Fergyanto E. Gunawan
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-09
publication date: 2025-10-09
starting page: 02504016
ending page: 02504016
doi: 10.26877/asset.v7i4.2254

Predicting Habitat Suitability of Mahseer Fish (Tor spp.) in Tropical River Systems Using MaxEnt and Google Earth Engine: A Geospatial Modeling Approach
type: item
description: Rivers are vital freshwater habitats that face threats of degradation and climate change. Mahseer fish, a key species, is in decline. This study predicted Mahseer fish habitats in Central Java using the Google Earth Engine and the MaxEnt machine learning algorithm. Environmental predictors, including NDVI, elevation, slope, river order, temperature, and rainfall, were extracted from Sentinel, SRTM, MODIS, and CHIRPS data. The model identified river order as the most influential variable (73%), followed by elevation (18%) and rainfall (8%), with an AUC score of 0.7, indicating fair accuracy. Suitable habitats were located in upstream river orders (1–3), typically at higher elevations. These findings provide spatial guidance for conservation planning, such as identifying critical habitats, prioritizing upstream areas, and establishing seasonal fishing ban. This approach supports biodiversity protection and aligns with the Sustainable Development Goals by offering a scalable tool for freshwater ecosystem management. Using MaxEnt with GEE shows promise for rapid, and cost-effective species distribution modeling in data-limited tropical regions.
subject: Articles
  topic: section
subject: Mahseer Fish
  topic: keywords
subject: Habitat Prediction
  topic: keywords
subject: Google Earth Engine
  topic: keywords
subject: Remote Sensing
  topic: keywords
subject: MaxEnt Machine Learning
  topic: keywords
creator: Jefri Permadi
creator: Nia Kurniawan
creator: Diana Arfiati
creator: Agung Pramana Warih Marhendra
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-08-23
publication date: 2025-08-23
starting page: 02503021
ending page: 02503021
doi: 10.26877/asset.v7i3.2201

Biosorption of Chromiun in Batik Wastewater Using SCOBY Microbial Biomass: A Sustainable Bioremediation Approach
type: item
description: Batik wastewater poses an environmental threat due to hazardous heavy metals like lead, cadmium, and chromium (Cr). This study investigated the effectiveness of SCOBY (Symbiotic Culture of Bacteria and Yeast), a microbial consortium from kombucha production, in reducing Cr levels in batik wastewater. SCOBY is a promising biosorbent for heavy metals. The research aimed to assess SCOBY's ability to decrease Cr contamination in different types of batik wastewater (hand-drawn, stamped, and printed) over varying incubation times. Using a quasi-experimental approach, wastewater samples were collected from small and medium industries in Pekalongan City. Results showed that SCOBY effectively reduced Cr levels across all batik wastewater types and incubation periods. The most significant reduction occurred at 3 hours of incubation. Specifically, Cr levels decreased by 53% in hand-drawn batik wastewater, 44% in stamped batik wastewater, and an impressive 71% in printed batik wastewater. These findings suggest that SCOBY treatment is a viable and effective alternative for managing batik wastewater.
subject: Articles
  topic: section
subject: batik wastewater
  topic: keywords
subject: bioremediation
  topic: keywords
subject: heavy metals
  topic: keywords
subject: kombucha
  topic: keywords
creator: Nur Lu’lu Fitriyani
creator: Dina Adelia
creator: Slamet Budiyanto
creator: Ristiawati
creator: Jaya Maulana
creator: Muhammad Choiroel Anwar
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-08-23
publication date: 2025-08-23
starting page: 02503020
ending page: 02503020
doi: 10.26877/asset.v7i3.2199

Development of an IoT-based Soil Nutrient Monitoring and GIS Mapping System for Precision Agriculture
type: item
description: Agriculture is a field that contributes to Indonesia's economic development. Unpredictable weather, temperature fluctuations, and the difficulty in assessing soil quality hinder farmers in enhancing crop productivity. The IoT in signifies a beneficial progression that will assist farmers in their endeavors. Precision agriculture is an innovative approach that employs information technology for sustainable agricultural management. This research aims to assess soil nutrients and provide mapping data based on the evaluated agrarian sites. The testing sites are situated in three sub-districts within Kubu Raya Regency: Sungai Kakap, Ambawang, and Rasau Jaya. The soil study indicated a temperature range of 29.40 °C to 36.80 °C. Soil moisture varied from 4 % to 89.10 %. The soil pH varied between 6.90-8.07 PH. The soil salinity was rather modest. Nutrient levels, particularly nitrogen, were slightly lower than those of phosphate and potassium, necessitating fertilizer use to enhance plant vegetative development. Incorporating the Internet of Things onto agricultural land delivers data as real-time monitoring, which will be essential for improving agricultural output. This scalable method mitigates contemporary agricultural difficulties by diminishing environmental impact and enhancing crop resilience. This study facilitates sustainable, intelligent agricultural techniques to address the escalating needs of a swiftly expanding global population.
subject: Articles
  topic: section
subject: geographic information system
  topic: keywords
subject: internet of things
  topic: keywords
subject: precision agriculture
  topic: keywords
subject: real-time monitoring
  topic: keywords
subject: soil nutrient
  topic: keywords
creator: Asrul Abdullah
creator: Eka Indah Raharjo
creator: Muhammad Iwan
creator: Rizki Faizal
creator: Maryogi
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-10-31
publication date: 2025-10-31
starting page: 02504037
ending page: 02504037
doi: 10.26877/asset.v7i4.2191

Determinants of MSME Sustainability: A Regression-Based Study in Labuhanbatu
type: item
description: This study aims to identify factors that influence the development of healthy and independent MSMEs in Labuhanbatu Regency, Indonesia. The research method uses a quantitative approach with primary data collection through interviews and questionnaires distributed to 342 MSME actors in Labuhanbatu Regency. The sample was taken using a stratified random sampling technique based on the 2019 MSME population. The data were analyzed using multiple regression techniques to test the effect of the independent variables partially and simultaneously on the dependent variable. The results showed that education and skills (β = 25.299, p < 0.01), locus of control (β = 4.452, p < 0.01), government support and policies (β = 18.001, p < 0.01), and access to capital and financial resources (β = 9.332, p < 0.01) have a positive and significant influence on the development of healthy and independent MSMEs. In contrast, financial literacy (β = 1.025, p > 0.1), partnership networks (β = 1.005, p > 0.1), and infrastructure and technology (β = 1.087, p > 0.1) did not contribute significantly in this study. These findings emphasize the need for human resource capacity building and government policies that support access to capital as key strategies to encourage the sustainability and self-reliance of MSMEs in the region.
subject: Articles
  topic: section
subject: MSME Resilience
  topic: keywords
subject: Regression Analysis
  topic: keywords
subject: Entrepreneurship Development
  topic: keywords
subject: Small Business Sustainability
  topic: keywords
subject: Entrepreneurial Autonomy
  topic: keywords
creator: Mulkan Ritonga
creator: Ade Parlaungan Nasution
creator: Rahma Muti’ah
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-09-04
publication date: 2025-09-04
starting page: 02504018
ending page: 02504018
doi: 10.26877/asset.v7i4.2180

Prediction of Soil Nutrients from Different Soil Textures using Portable Spectrometer and Machine Learning
type: item
description: Soil nutrients, such as nitrogen, phosphorus, and potassium, are critical for plant growth and agricultural productivity. Conventional laboratory methods for measuring these nutrients are accurate but often time-consuming, costly, and environmentally taxing. This study explores the potential of portable visible-near infrared (Vis-NIR) spectrometer combined with machine learning algorithms as a rapid, cost-effective, and eco-friendly alternative for soil nutrient analysis. Soil samples of clay, clay loam, and sandy clay were collected and analyzed using artificial neural network (ANN) approach to predict soil nutrients. A total of 81 reflectance spectra data from each soil type were acquired using an AS7265x sensor and processed to develop a predictive model for nutrient content. ANN models demonstrated high accuracy, with R² values exceeding 0.8 in each type of soil texture. This study emphasizes the potential of portable Vis-NIR spectrometer and machine learning integration to revolutionize soil nutrient analysis, offering significant improvements in agricultural efficiency and sustainability.
subject: Articles
  topic: section
subject: Artificial Neural Network
  topic: keywords
subject: machine learning
  topic: keywords
subject: portable vis-nir spectrometer
  topic: keywords
subject: soil nutrient prediction
  topic: keywords
subject: spectral analysis
  topic: keywords
creator: Harki Himawan
creator: Rut Juniar Nainggolan
creator: Handono Rakhmadi
creator: Gunomo Djoyowasito
creator: Ubaidillah
creator: Lenny Sri Nopriani
creator: Dimas Firmanda Al Riza
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-12-23
publication date: 2025-12-23
starting page: 0260103
ending page: 0260103
doi: 10.26877/asset.v8i1.2166

Comparative Analysis of Observed and Empirical Rainfall Distribution for Flood Hydrograph Modeling
type: item
description: Flood disasters in Indonesia are persistent challenges during the rainy season, primarily due to intense rainfall and inadequate flood control. This study evaluates hourly rainfall to characterize hydrology and predict flood discharge more accurately, benefiting water infrastructure planning. The research used modified Mononobe methods, observational data, and rainfall-runoff modeling, including HEC-HMS simulations with the SCS-CN unit hydrograph. Observed rainfall simulated a flood discharge of 779.7 m³/s, while empirical rainfall yielded 3623 m³/s, showing a 79.12% deviation. Comparing flood hydrographs, recorded rainfall data closely matched previous studies (R² = 0.94), unlike empirical rainfall (R² = 0.88). The study concludes that observed rainfall is highly effective for estimating flood runoff, accurately representing local characteristics. This method significantly aids planning and design of water resource infrastructure like dams, weirs, and bridges at the study site.
subject: Articles
  topic: section
subject: Empirical Rainfall
  topic: keywords
subject: Flood Modeling
  topic: keywords
subject: HEC-HMS
  topic: keywords
subject: Observed Rainfall
  topic: keywords
subject: SCS-CN
  topic: keywords
subject: Rainfall-Runoff
  topic: keywords
creator: Risdiana Afifah
creator: Sri Sangkawati
creator: Suripin
creator: Dyah Ari Wulandari
rights: Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
date: 2025-08-23
publication date: 2025-08-23
starting page: 02503026
ending page: 02503026
doi: 10.26877/asset.v7i3.2142