Advance Sustainable Science Engineering and Technology
Journal Title Advance Sustainable Science, Engineering and Technology (ASSET) Frequency 4 issues per year (January, April, July, October) DOI Prefix 10.26877 by ISSN 2715-4211(Online) Editor-in-chief Mega Novita, S.Si., M.Si., M.Nat.Sc., Ph.D Publisher Universitas PGRI Semarang Indexing SCOPUS| Sinta 1 | Dimensions | Crossref | Garuda | Google Schoolar | Scimagojr (Q4) This page is a new page for an old journal https://journal.upgris.ac.id/index.php/asset Advance Sustainable Science, Engineering and Technology (ASSET) is a peer-reviewed open-access international scientific journal dedicated to the latest advancements in sciences, applied sciences and engineering, as well as relating sustainable technology. This journal aims to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of sciences, engineering, and technology. ASSET papers will be published twice a year by Universitas PGRI Semarang, Jawa Tengah, Indonesia.
Science and Technology Research Centre Universitas PGRI Semarang
Design and Development of SIPADMA: A Software Engineering Approach for Water Consumption Anomaly Detection in Public Water Utilities
Water utilities continuously face challenges in identifying abnormal customer water consumption caused by pipe leakage, illegal connections, meter reading errors, and unusual consumption behavior. Conventional manual inspection requires considerable time and often delays corrective actions. This study presents the design and development of SIPADMA (Sistem Deteksi Anomali Pemakaian Air), a web-based information system developed using Software Engineering principles to automate anomaly detection and support operational decision making. The system was developed following the Waterfall Software Development Life Cycle (SDLC), beginning with requirement analysis, UML-based system modeling, implementation using Flask, and functional testing. SIPADMA integrates statistical anomaly detection using Z-Score combined with rule-based scoring and provides interactive dashboards, anomaly filtering, customer history visualization, and automatic report generation. Evaluation using 3,996 billing records representing 666 customers demonstrated that the system successfully identified 163 anomalous accounts requiring further inspection. The developed system improves operational efficiency by automating data analysis and prioritizing field verification, demonstrating the effectiveness of software engineering practices in developing intelligent decision-support systems for public utilities.
Copyright (c) 2026 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
A Comparative Evaluation of Base Isolation Effectiveness in Mitigating Seismic Response for Low-Rise and Mid-Rise Buildings
Low-rise and mid-rise structures often possess natural periods that coincide with high-energy seismic response plateaus, rendering them susceptible to significant response amplification. This study evaluates the comparative effectiveness of High Damping Rubber Bearings (HDRB) in mitigating these risks for 4-story and 8-story office buildings under Indonesian seismic conditions. Using rigorous Non-Linear Time History Analysis (NLTHA) in the dominant X-direction, the research examines period elongation, lateral displacement, inter-story drift, peak floor acceleration, and base shear attenuation. Scientific findings reveal that HDRB transitions structural behavior into a near-rigid-body motion, reducing normalized inter-story drift to 0.26 and 0.12, and base shear by up to 71%. Furthermore, peak floor acceleration is curtailed by 51% to 59%, ensuring the protection of sensitive non-structural components. The results demonstrate that while base isolation provides superior seismic protection for both building scales, its efficacy in intercepting seismic energy and preventing operational disruptions is significantly more pronounced in mid-rise structures. This study confirms that HDRB is an increasingly vital strategy for enhancing functional resilience as building height increase.
I Gusti Ngurah Agung Eka Arya Tejadinata
Prediction of Ground Vibration due to Blasting Activities of Coal Open Pit Mine Near Village Residents Using Multivariate Logarithmic Regression
Blasting activities are among the most important in mining and can have a negative impact on the environment and surrounding communities, causing disruption and even damaging nearby buildings and infrastructure. If the community protests and demonstrates, mining operations may be shut down, which would be very detrimental to the company. Studies are needed on effective planning to reduce the negative impacts. Ground vibrations measured by Peak Particle Velocity (PPV) are subject to thresholds set by each region's standards; in this research area, the maximum threshold is 3 mm/s to avoid damage to nearby buildings or settlements. The important variables are the explosive charge per delay and the distance, along with other blasting geometry variables such as spacing, burden, stemming, powder factor, and number of blast holes. Several previous researchers have used methods to predict PPV, with detonation parameters as the independent variables. This research uses general empirical methods and multivariate logarithmic regression (MLR). Prediction using MLR is better than general empirical methods; with R2 = 0.925, RMSE = 0.247, MAE = 0.548, MAPE = 0.218, and VAF = 96.66%, indicating near-perfect prediction. The MLR model produces a reference maximum explosive charge per delay of 59.09 kg.
CFD-Based Thermohydrodynamic Analysis of Vegetable Oil Lubricants in Journal Bearings
This study investigates the thermohydrodynamic performance of journal bearings lubricated with palm trimethylolpropane (TMP) ester as a biolubricant, compared to conventional engine oil, using computational fluid dynamics (CFD) in ANSYS Fluent. The objective is to evaluate the influence of lubricant type on pressure distribution, load carrying capacity, friction force, and cavitation behavior. The results show that palm TMP ester generates higher hydrodynamic pressure while maintaining a similar pressure distribution pattern to engine oil. The load carrying capacity increases significantly by approximately 493% at 48 rad/s and 343% at 68 rad/s compared to engine oil. However, this improvement is accompanied by an increase in friction force of about 280% and 234% at the respective speeds due to higher viscosity. In addition, the vapor volume fraction ranges from 0.69 to 0.73, indicating cavitation, with palm TMP ester showing a slightly higher tendency. These findings demonstrate a trade-off between enhanced load support and increased friction, highlighting the potential of palm TMP ester as an environmentally friendly lubricant for hydrodynamic bearing applications.
CFD Simulation of Flame Characteristics Resulting from Volatile Matter Combustion of Various Biomass Pellets
While biomass is a promising carbon-neutral alternative to coal, the specific volatile matter (VM) flame characteristics of diverse biomass pellets, particularly water hyacinth, remain under-researched. This study uses a numerical CFD approach (Ansys Fluent) to investigate how varying VM fractions influence flame structure in a 2D planar slice of the furnace block (25 cm width). Simulations employed the SST k–ω turbulence and Eddy dissipation model to capture mixing-limited chemical reactions. Boundary conditions were based on experimental configurations using a 0.05 m/s air inlet velocity. Results using CO-based flame-tip markers revealed that water hyacinth (VM: 63.5 wt%) produced a peak temperature of ~1,400°C at 75 cm above the fuel, while rice husk and bagasse (VM: 59–77 wt%) exhibited longer, more intense hot plumes compared to the localized heat profile of coal. These findings demonstrate that biomass generates more dispersed combustion zones, aiding in furnace hot-spot prevention and air control optimization. A limitation of this study is that findings are based solely on numerical simulations without direct experimental validation, although the model replicates physical furnace configurations. These results provide a foundation for developing sustainable biomass–coal co-firing technologies.
Integrated Sustainable Manufacturing and Waste Management Framework for Medium-Density Fiberboard (MDF): Finite Element Methods-Based Structural Optimization for Bookshelf Applications
Medium-density fiberboard (MDF) is commonly used in furniture manufacture because of its consistent qualities, low cost, and ease of processing. However, its relatively short lifespan and rising market demand have resulted in substantial waste generation, posing serious environmental and disposal concerns. This study provides an integrated sustainable manufacturing and waste management framework for MDF that incorporates artificial intelligence technology and finite element method (FEM)-based structural optimization for bookshelf applications. The structural performance is evaluated by numerical simulations focusing on von mises stress, displacement, and safety factor. These findings indicate that combining FEM-based design optimization with intelligent waste management strategies might enhance the structural performance and sustainability of MDF products. This study emphasizes the necessity of merging advanced simulation, artificial intelligence, and life-cycle assessment methodologies to create intelligent, efficient, and ecologically responsible wood-based manufacturing systems.
ECCFD-GNN: A Novel Risk-Sensitive Graph Neural Network Model for Fraudulent Transaction Detection
The study presents the integration of machine learning techniques for detecting the credit card fraud. Its integration maintains a behavioral profile of cardholders and other parameters like location, frequency, amount etc. resulting in the timely detection of any anomaly from the normal behavior. A novel approach ECCFD-GNN (Enhanced Credit Card Fraud Detection based on Graph Neural networks) is proposed enhancing the performance of fraud detection. The various behavioral indicators taken into consideration are number of months with late payments, the frequency of low payments and the length of the account, which are further combined to a newly introduced feature “risk score”. The purpose of risk score is to increase the model’s sensitivity to the transactions having complex fraud risks. The approach uses three Graph Neural Network architectures namely KNN graph GNN, Radius Graph GNN and Feature Correlation GNN. The experiment is performed with both the optimizers Adam and RAdam. With Adam optimizers the results show that KNN graph GNN provides better performance when compared on the basis of different evaluation parameters with accuracy 85%, precision 76% recall 70% and F1-score as 73%. The results are improved when tested with RAdam optimizers leading to increased accuracy, precision, recall and F1 score.
Comparative Deep Learning Models for Indonesian Gold Price Forecasting
This study evaluates LSTM, CNN-LSTM, LSTM-GRU, and CNN-LSTM-GRU architectures for forecasting Indonesian gold prices using 1,269 daily observations (2022–2025). Models utilized Bayesian-optimized hyperparameters and were benchmarked against ARIMA-GARCH and Random Forest baselines across 30-day and 365-day horizons. Performance was assessed via MAE, RMSE, R², and MAPE, confirming deep learning’s superiority in capturing non-linear dynamics over classical methods. The LSTM-GRU achieved the best numerical results, with MAPEs of 1.21% (short-term) and 1.32% (long-term). However, qualitative evaluation revealed that the highest-scoring model produced unstable long-term predictions, indicating a critical trade-off between numerical accuracy and forecast realism. These findings suggest financial model selection must prioritize stability alongside statistical metrics. A key limitation is the exclusive use of univariate data, necessitating future multivariate validation with macroeconomic indicators.
Bottleneck Analysis and Improvement in Apparel Manufacturing Production Processes Using Integration Design of Experiments and Discrete Event Simulation
Bottlenecks in apparel manufacturing often cause unbalanced production flows, increased waiting times, and reduced system performance. This study aims to analyze and eliminate bottlenecks by integrating Design of Experiments (DOE) and Discrete Event Simulation (DES). Four workstations (X1–X4) were selected as experimental factors, while system performance was evaluated using bottleneck indicators across six production stages (Y1–Y6). DOE was used to design capacity scenarios, and DES assessed system performance under each configuration. Results show that partial capacity increases at selected workstations are insufficient to fully eliminate bottlenecks. Complete elimination was achieved only in specific scenarios (Experiments 13–16), where all bottleneck indicators reached zero. Among these, Experiment 13 was identified as the optimal solution, as it eliminated all bottlenecks with the minimum additional capacity. These findings indicate that targeted capacity enhancement at critical workstations is an effective and economical strategy. The integration of DOE and DES proves to be a reliable data-driven approach for identifying bottlenecks and selecting optimal capacity improvements. This study also provides a structured and replicable framework for bottleneck analysis in apparel manufacturing, contributing to the limited application of DOE–DES integration in this sector.
GIS-Based Spatial Equity Assessment of Temporary Waste Disposal Sites in Riverbank Informal Settlements: A Case Study of Palembang, Indonesia
Rapid population growth and urbanization in informal settlements along the Musi Riverbank, Palembang City, have intensified pressure on the waste management system, particularly due to the uneven distribution of Temporary Disposal Sites (TPS) and limited collection services. This study, conducted in June–July 2025, examines the spatial conditions of TPS and waste management through a quantitative methods approach that integrates Geographic Information Systems (GIS) with a survey of 385 respondents across 13 sub-districts. Results indicate that TPS facilities remain concentrated in densely populated areas, while Gandus and Kertapati exhibit substantial service gaps, with 63.1% of residents unaware of any TPS near their area and 20.3% reporting inadequate TPS availability. Spatial analysis shows that only 34.0% of households live within 0–0.5 km of a TPS, while 24.4% are located more than 1 km away, indicating significant underserved zones. The study recommends GIS-based TPS redistribution and stronger community engagement mechanisms as actionable strategies to support evidence-based municipal waste planning and policy.
Copyright (c) 2025 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Scalable TOPSIS Variants in Web-Based Decision Support Systems: A Performance Benchmarking Study on Vectorization and Uncertainty Modelling
This study systematically benchmarks the computational performance and decision consistency of three TOPSIS variants—classical TOPSIS, vectorized TOPSIS, and Z-TOPSIS—in scalable web-based multi-criteria decision support systems (DSS). Although TOPSIS is widely used due to its simplicity and interpretability, its scalability and computational behavior under concurrent web workloads remain insufficiently explored. To address this gap, the algorithms were evaluated using datasets containing 50–500 alternatives and 10 criteria, representing realistic decision-making scenarios. Classical TOPSIS was used as the baseline, vectorized TOPSIS applied matrix-based optimization, and Z-TOPSIS incorporated Z-numbers to capture uncertainty. Experiments were conducted on a cloud-based DSS equipped with multi-core CPUs and 16–32 GB RAM, measuring execution time, throughput, response time, and ranking consistency under workloads of 50–500 concurrent users. The results show that vectorized TOPSIS reduced execution time by approximately 50–55% (26.3 ms vs. 57.9 ms) and achieved the highest throughput of 480 requests per second. In contrast, Z-TOPSIS produced higher latency (68.4 ms) due to additional reliability computations. Ranking consistency remained high between classical and vectorized TOPSIS (Kendall’s tau ≥ 0.98), while Z-TOPSIS showed minor deviations (tau = 0.91). These findings provide practical guidance for selecting TOPSIS variants in scalable web-based MCDM applications.
A Decision Support System Based on Transformer-Driven Sentiment Analysis of Social Media Data
The growing availability of social media data offers new opportunities for decision support systems (DSS) in large-scale human resource screening. This study proposes a technology-driven DSS architecture integrating transformer-based sentiment analysis to support early-stage candidate profiling. Its novelty lies in combining IndoBERT-based sentence embeddings with a structured DSS layer that aggregates tweet-level sentiment into risk-aware recommendations, rather than treating sentiment classification as a standalone output. Using a quantitative experimental design, 5,000 public posts from 100 users were processed through an NLP pipeline incorporating mean-pooled embeddings, feature engineering, principal component analysis, and Support Vector Machine classification. The model achieved 69.1% accuracy, with weighted precision, recall, and F1-score of 0.694, 0.691, and 0.691, outperforming baseline models by 6.5–15.0 percentage points. Sentiment outputs are treated as probabilistic behavioral signals within an advisory DSS framework, not direct indicators of candidate suitability. Preliminary validation on 50 cases showed moderate correlations (ρ = 0.52–0.61) with conventional assessments. The system remains non-automated, incorporating confidence thresholds, uncertainty handling, and mandatory human oversight. Limitations include moderate accuracy, reliance on text-only data, and linguistic ambiguity.
Hybrid XGBoost-LSTM Framework for Accurate SOC, SOH, DOD and Internal Resistance Estimation in Li-ion Cells
Accurate estimation of State of Charge (SOC), State of Health (SOH), Depth of Discharge (DOD), and internal resistance is critical for Battery Management Systems (BMS) in electric vehicles and energy storage. Conventional methods fail to capture the nonlinear and temporal dynamics of lithium-ion cells, while existing machine learning approaches lack systematic benchmarking for embedded deployment. This study evaluates three hybrid models XGBoost-LSTM, XGBoost-SVR, and Linear Regression-Random Forest on high-resolution Samsung 30T single-cell data (five cycles, 6,081 timesteps). Models used 35 mutual information-selected features, identical preprocessing, and Bayesian hyperparameter optimization. XGBoost-LSTM achieved superior accuracy: SOC (R²=0.983), SOH (R²=0.985), DOD (R²=0.977), and internal resistance (R²=0.972), outperforming baselines significantly (Wilcoxon p<0.05). Computational profiling showed 15 ms inference latency and 60 MB memory usage, suitable for real-time BMS at 10 Hz. Results indicate that hybrid temporal learning improves battery diagnostics, while further validation across multiple chemistries, extended temperatures, multi-cell setups, and longer cycles is recommended for practical deployment.
Supply Chain Performance Evaluation in the Pulley Manufacturing Industry Using Supply Chain Operations Reference and Analytical Hierarchy Process
Supply chain performance is critical to the efficiency of make-to-order manufacturing systems, where waste such as waiting, rework, unnecessary motion, overprocessing, and transportation still occurs at several workstations. These inefficiencies prolong production time and reduce overall effectiveness. This study aims to measure supply chain performance, determine the relative importance of performance metrics, and establish improvement priorities. The research integrates the Supply Chain Operations Reference (SCOR) model with the Analytical Hierarchy Process (AHP) to evaluate performance and assign objective weights to key indicators. The analysis focuses on five critical workstations identified as major sources of waste. The results provide an objective performance score, identify key indicators requiring improvement, and offer prioritized recommendations to enhance efficiency. Findings show that indicators such as waiting time and facility layout efficiency still need improvement to reach an excellent performance level. The integration of SCOR and AHP effectively supports decision-making in determining improvement priorities, contributing to a more efficient and productive pulley manufacturing process. This study also offers a structured approach to performance evaluation in make-to-order systems, providing practical insights for decision-makers in Small and Medium Industries (SMIs).
Camera-Based Smart Mirror with Machine Learning for Postural Analysis: System Development and Reliability Evaluation
Early postural assessment using camera-based systems remains technically challenging due to variability in user positioning and limited evaluation of measurement repeatability. This study presents the development and repeatability evaluation of a smart mirror system for automated postural analysis using pretrained pose estimation and rule-based geometric classification. The system consists of a fixed camera mounted above a mirror and a connected computing device for real-time processing and visual feedback. Anatomical landmarks were detected from standardized anterior, posterior, and lateral views using an AI-based pose estimation model, and postural asymmetry was quantified using bilateral distance ratios and angular deviation thresholds derived from literature. Reliability was evaluated through repeated measurements to assess the consistency of landmark detection and postural classification outputs. Forty adolescents (age 12.8 ± 0.56 years; 28 males, 12 females) participated in present study. The system intra-rater reliability was evaluated by calculating Intraclass Correlation Coefficients (ICC) for the landmark data and Cohen's Kappa for posture classifications. The system demonstrated excellent reliability for key landmarks in scapula (ICC = 0.98, 95%CI 0.97-0.99) and hip-knee-ankle (ICC = 0.98, 95%CI 0.98-0.99). The classifications for scoliosis assessment also showed excellent agreement (κ = 0.90). These results indicate that the proposed system can produce repeatable posture measurements under controlled conditions; however, this study evaluates repeatability only and does not assess diagnostic accuracy or clinical validity. Further validation against clinical reference standards is required before broader application.
Metaheuristic Optimization Stacking Application for Rainfall Classification: A Comparative Study
Accurate rainfall classification plays a vital role in effective meteorological forecasting, agricultural planning, and also to avoid natural disasters. However, standard classification models often exhibit unstable performance. This study evaluates the effectiveness of an Ensemble Stacking framework enhanced with Optimized Swarm Based and Metaheuristic method such as Artificial Bee Colony (ABC) and Cuckoo Search (CS) optimization algorithms to improve prediction reliability. The proposed approach was tested using a detailed rainfall dataset by combining basic classification models, such as Decision Tree, SVM, Naive Bayes, and kNN. The results show that Stacking Ensemble generally outperform individual basic models in Accuracy and F1 Score (reaching a median > 0.80), while unoptimized Stacking method show low variances but provides a less better result in terms of Accuracy and F1 Score. In contrast, the Stacking model optimized with ABC emerged as a better method, demonstrating the highest stability and significantly reducing the performance distribution range compared to the non-optimization and Cuckoo Search scenarios. These findings conclude that the application of Artificial Bee Colony optimization to Stacking ensembles effectively minimizes prediction variance, making it the most reliable strategy for consistent rainfall forecasting using classification modelling technique.
Effect of NaOH Molarity on Gel Formation at the Geopolymer–Artificial Aggregate Interface for Pavement Applications
This study investigates the effect of NaOH molarity (10, 12, and 14 M) on gel formation at the geopolymer–artificial aggregate interfacial transition zone (ITZ) for sustainable pavement binder courses. The study focuses on microstructural and chemical characteristics rather than direct mechanical performance. Using XRF, FTIR, and SEM–EDX analyses, the evolution of gel phases from N-A-S-H to hybrid N/C-A-S-H at the interface was systematically characterized. Increasing NaOH molarity enhanced aluminosilicate dissolution and promoted Ca incorporation, as evidenced by the shift of the main Si–O–T band from approximately 1080 cm⁻¹ (10 M) to 960–970 cm⁻¹ (14 M) and the increase in Ca/Si ratio from 0.15 to 0.22. Among the investigated mixtures, the 12 M variant exhibited the most homogeneous and compact ITZ microstructure, characterized by a continuous reaction rim, balanced Si/Al (2.96) and Ca/Si (0.22) ratios, and minimal microcracking observed at the microscopic scale.
Development and Performance Evaluation of a Micro-Scale RDF Briquette–Fueled Steam Power Prototype
Plastic waste is a major environmental problem in Indonesia due to its non-biodegradable nature. One innovative solution is converting waste into energy using Refuse-Derived Fuel (RDF) briquettes for small-scale power generation. This research designed and tested an RDF-based micro power plant prototype using briquettes composed of 80% dry organic biomass and 20% plastic for safe and stable combustion. The prototype consists of a combustion chamber, heat exchanger, impulse-type micro steam turbine driven by low-pressure steam, and a 12V low-speed DC generator. Performance was evaluated through temperature, voltage, current, power output, and efficiency measurements. The highest performance was achieved using 500 g of RDF, producing 0.02 A, 0.7 V, and 0.014 W at 135°C over 20 minutes, with an efficiency of 2.69×10⁻⁶%. Although efficiency was very low, the study demonstrates proof-of-concept feasibility and provides a baseline for future optimization of thermal and energy conversion efficiency.
Integrated 1D-2D Resistivity Inversion for Mapping Seawater Intrusion in a Coastal Aquifer: Kenjeran-Surabaya, Indonesia Case Study
This study investigates the extent of intrusion in the Kenjeran coastal aquifer, Surabaya, Indonesia, through an integrated geophysical approach. Four one-dimensional Vertical Electrical Sounding (VES) surveys and two two-dimensional Electrical Resistivity Tomography (ERT) transects were conducted using Schlumberger and Wenner–Schlumberger configurations to obtain both depth-specific and laterally continuous resistivity data. The 1D VES results detected low-resistivity layers (<1.0 Ω·m) at depths exceeding 58–66 m, indicating deep saline groundwater. The 2D ERT sections identified wedge-shaped low-resistivity anomalies (0.1–0.8 Ω·m) at depths of 7.5 m to 48 m, indicating active intrusion progressing inland. Intrusion is more severe in the northern sector, with vertical penetration up to 48 m and horizontal encroachment beyond 200 m from the shoreline. The integration of 1D and 2D resistivity imaging proved effective in delineating saline–freshwater interfaces, enabling targeted mitigation measures and informed groundwater management to safeguard Kenjeran’s aquifer from further degradation.
Kernel Density and Spatial Modeling of Informal Settlement Concentration: Methodology and Findings from Palembang, Indonesia
Rapid urbanization has intensified the growth of slum settlements in Indonesian cities, including Palembang, where informal housing commonly develops along riverbanks. This study aimed to identify and evaluate the spatial distribution and density of slum areas in Palembang City through a Geographic Information System (GIS)–based approach combining Kernel Density Estimation (KDE) and Receiver Operating Characteristic–Area Under Curve (ROC–AUC) analysis. Primary spatial data were obtained from 382 household survey points representing 64 slum polygons across 13 sub-districts, supplemented by administrative boundary and land-use data from the Palembang City Government. Spatial analysis and validation were conducted using ArcGIS 10.3 software. The KDE results showed density values ranging from 0 to 58.1123 units per 100 m², with the highest concentrations found along the Musi River corridor, decreasing outward from the riverbanks. Model validation achieved an AUC value of 0.968 (96.8%), demonstrating excellent predictive accuracy. These spatial outcomes provide actionable guidance for policymakers by identifying priority zones for sanitation and drainage upgrades, flood-resilient housing design, and targeted relocation planning. The study highlights the practical role of GIS-based quantitative modelling in supporting evidence-based slum management and urban infrastructure planning in Indonesia.
FTFPOS-IDF: A Fuzzy Rule-Based Thematic Term Weighting Scheme for Bloom's Taxonomy Question Classification
The increasing adoption of Artificial Intelligence (AI) in education has created a growing demand for automated and reliable assessment systems. Existing Bloom’s Taxonomy (BT) question classification approaches commonly rely on TF-IDF-based weighting schemes, which assign static term weights and often fail to capture the varying thematic importance of terms across cognitive levels. To address this limitation, this study proposes a novel Fuzzy Thematic Feature and Part-of-Speech Inverse Document Frequency (FTFPOS-IDF) weighting scheme that integrates fuzzy rule-based reasoning with Natural Language Processing (NLP) to dynamically assign thematic weights according to Bloom’s Taxonomy relevance. The proposed framework combines Machine Learning (ML) and Deep Learning (DL) classifiers with Chi-Square feature selection to reduce irrelevant features and improve classification performance. Experimental results demonstrate that FTFPOS-IDF consistently outperforms conventional TF-IDF variants across multiple classification models. The highest performance was achieved by the Multilayer Perceptron (MLP) classifier with an accuracy of 86.7%. These findings indicate that fuzzy rule-based thematic weighting can effectively enhance Bloom’s Taxonomy question classification and support scalable, reliable, and sustainable digital assessment systems in educational environments.
Data-Driven Techno-Behavioral Segmentation of Post-Pandemic Tourists Using TwoStep Cluster Analysis
Post-pandemic tourism is characterized by increasing behavioral heterogeneity as digital technologies reshape travel planning and mobility practices, challenging traditional demographic-based segmentation. This study develops a techno-behavioral, data-driven segmentation framework within the Smart Tourism Ecosystem perspective by conceptualizing digital adoption as a mediating mechanism between socio-demographic attributes and travel behavior. Using survey data from 805 domestic tourists in Yogyakarta, Indonesia, TwoStep Cluster Analysis (log-likelihood distance; BIC-based cluster selection) identifies two distinct segments: Digital Leisure Travelers (DLT) and Budget-Conscious Digital Natives (BDN). The clustering solution demonstrates fair quality (Silhouette = 0.32). Predictor-importance and validation tests indicate that income, education, generational cohort, and digital application use are the strongest discriminators, while itinerary intensity differs significantly between clusters (p < 0.001; η² = 0.10). The findings highlight that widespread digital engagement produces differentiated mobility outcomes shaped by socio-economic capacity, emphasizing the need for segment-sensitive and inclusive smart tourism strategies.
Uncertainty-Quantified Grid-Convergence Analysis of RANS Turbulence Models for 2-D Incompressible Backward-Facing Step Flow in OpenFOAM
A concise evaluation of Reynolds-Averaged Navier–Stokes (RANS) turbulence modeling for two-dimensional, incompressible, steady backward-facing step (BFS) flow at Re = 1000–3000 was conducted using OpenFOAM’s SimpleFoam solver with the standard k–ε model. A tri-level mesh enhancement (coarse, medium and fine) was implemented, and ambiguity was measured utilizing the Convergence Ratio (CR) and Grid Convergence Index (GCI). The fine grid (CR = 0.54; GCI = 0.0059%) was the only configuration exhibiting monotonic convergence, ensuring valid GCI estimation. Results showed reattachment length increasing from 0.11 m to 0.12 m, with stronger vortical structures and steeper shear gradients at higher Re. This study uniquely integrates RANS model validation with grid-uncertainty quantification, providing guidance for mesh optimization and reliable turbulence modeling in BFS simulations.
Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
A CIA-based Sustainable Security Risk Mitigation Model for E-Certificate Systems
E-certificates are increasingly adopted across sectors, yet existing studies have not developed an integrated risk mitigation model that combines CIA-based sustainable security with operational and stakeholder perspectives. Current frameworks primarily address isolated technical risks or focus on general PKI security, leaving a gap in holistic modeling tailored to end-to-end e-certificate implementation. This study addresses this gap by proposing a Sustainable Security Risk Mitigation Model for e-certificate systems, guided by the CIA triad—Confidentiality, Integrity, and Availability. A mixed-methods approach was employed, including literature analysis, a Focus Group Discussion (FGD) with industry, government, and academic stakeholders, and expert evaluation using CIA-based scoring on a Likert scale. The empirical data include qualitative perspectives gathered from the FGD and quantitative assessments from expert validation. The proposed model operates in a continuous cycle consisting of risk assessment, mitigation planning, deployment and monitoring, and iterative improvement, ensuring that security controls adapt to emerging threats. Results show that the model achieves an average security validation score (asv) of 4.67, outperforming other existing risk mitigation models in CIA-based evaluation. The findings indicate that institutions can use the model as a practical framework to strengthen e-certificate governance, improve resilience against cyber threats, and support sustainable information security management.
A Hybrid AI–SEMPLS Model for Digital Visualization Acceptance in Blue Tourism: Evidence from Lampung Province
Blue tourism destinations often lack advanced digital tools capable of providing real-time, AI-driven visualization and user-centered information services. This study addresses this gap by developing JELAMBU, an AI-enabled digital visualization platform, and by evaluating user acceptance through a hybrid SEMPLS models. The research aims to: (i) design and implement an AI-based system that combines chatbot interaction, realtime sentiment analytics, and digital visualization; and (ii) examine the determinants of tourists’ intention to adopt AI-enabled e-tourism technologies. A structured questionnaire was administered to 467 visitors of destinations, and 16 hypotheses were tested. The results show that platform design, facilitating conditions, AI technology, perceived ease of use, perceived usefulness, social influence, service quality, trust, and risk perception significantly shape intention to use, whereas information quality, perceived benefits, and performance expectancy do not show significant effects. The model demonstrates substantial predictive power (R² = 0.703), strong effect sizes (f² > 0.225), and acceptable fit (SRMR = 0.084). These findings highlight the pivotal role of design and system conditions in AI-driven tourism platforms and provide practical guidance for developers and policymakers in strengthening digital visualization, personalization features, and sustainable blue tourism management. Future studies may extend this framework to multi-regional settings or longitudinal adoption scenarios.
Microencapsulation of Phycocyanin from Spirulina platensis by Freeze-Drying: Optimization of Maltodextrin–Soy Protein Matrices for Enhanced Stability and Antioxidant Functionality
Phycocyanin, a natural blue pigment from Spirulina platensis, exhibits strong antioxidant activity but is highly unstable under light, heat, and pH variations, limiting its practical applications. This experimental study addresses the lack of systematic optimization data on maltodextrin–soy protein isolate (SPI) wall matrices for phycocyanin microencapsulation via freeze-drying. Phycocyanin was extracted using phosphate buffer and encapsulated at different maltodextrin:SPI ratios (9:1, 8:2, and 7:3). Each formulation was analyzed in triplicate (n = 3) for encapsulation efficiency (EE), phycocyanin retention, moisture content, particle size, and antioxidant activity (DPPH assay). The 8:2 ratio exhibited the best performance with EE of 88.5%, phycocyanin content of 0.710 mg·mL⁻¹, and particle size of 70.2 µm. Moderate antioxidant activity was observed (IC₅₀ = 102.29 ppm). ANOVA confirmed that the polymer ratio significantly affected all parameters (p < 0.05). Overall, the optimized maltodextrin–SPI microcapsules enhanced the stability and antioxidant functionality of phycocyanin under laboratory conditions, supporting their potential application as bioactive ingredients in functional food and pharmaceutical formulations.
Microwave-Pyrolysed Rice Husk-Derived Activated Carbon as a Sustainable Anode Material for Lithium-Ion Half-Cell Batteries
Agricultural waste biomass is explored as a sustainable precursor for activated carbon. In this study, rice husk-derived activated carbon (RHAC) was synthesized via microwave-assisted pyrolysis and KOH activation at 600, 700, and 800 °C. The KOH activation enhanced the thermal stability of the samples, with RHAC_700 exhibiting a spongy, interconnected structure and a surface area of 390.33 m² g⁻¹. When evaluated as a half-cell anode materials for lithium-ion batteries, RHAC_700 delivered an initial discharge capacity of approximately 388.33 ± 11.65 mAh g⁻¹ with Coulombic efficiency 68.29 % and stabilized at 250.00 ± 7.33 mAh g⁻¹ with 99.82% of Coulombic efficiency after 100 cycles at 0.2 A g⁻¹ and 25 °C (active material loading: 0.95 ± 0.15 mg cm⁻²). All reported values represent the average of three independent cells. Furthermore, RHAC_700 demonstrated good rate capability, retaining capacities from 248.33 ± 7.45 mAh g⁻¹ to 134.58 ± 4.04 mAh g⁻¹ at current densities ranging from 0.2 to 1.0 A g-1.
Analysis for Non-covalent Bonds in Protein Structures Using the DV-Xα Method
Halogen atoms are increasingly recognized for their ability to form non-covalent halogen bonds in protein–ligand complexesy. To systematically evaluate these interactions, we employed the DV-Xα method to calculate bond overlap populations (BOPs) in protein structures containing halogenated ligands. Using the Protein Data Bank, we identified thousands of entries with fluorine, chlorine, bromine, or iodine atoms. Structural coordinates were extracted around the chlorine atoms of the ligand and subjected to ab initio calculations under multiple truncation schemes. The findings highlight the utility of DV-Xα calculations in characterizing weak interactions and provide insights for future computational drug design employing halogen-containing compounds.
Graph-Theoretical Approach to Clusters – Analysis of Clusters with 5–11 Vertices –
In this study, we systematically analyzed all isomers of 5–11 atom clusters using a graph-theoretical approach and evaluated their maximum Hückel energy (HE) within the Hückel approximation. The maximum HE increased with the number of bonds, reached a maximum at an intermediate value, and then decreased again, with the peak located near the average of the minimum and maximum possible edge numbers. This relationship was well reproduced by a simple linear expression. The isomers with maximum HE were found to have nearly uniform degree distributions, and classification by graph radius (Gr) and maximum degree (Ma) revealed that they possess compact and homogeneous structural characteristics. By extrapolating these trends, structural features of maximum-HE isomers were predicted for clusters with 12–14 atoms. These results demonstrate the effectiveness of a graph-theoretical description for analyzing cluster stability and provide a foundation for extending the analysis to larger clusters and for comparison with first-principles calculations.
Oligonuclear Cobalt Assembly with Schiff-base Ligands
In order to develop molecular magnetic compounds, oligonuclear cobalt complexes with Schiff-base ligands, 1,3-bis(salicylideneamino)-2-propanol (H3salpr), 1,3-bis(5-chlorosalicylideneamino)-2-propanol (H3clsalpr), 1,3-bis(5-bromosalicylideneamino)-2-propanol (H3brsalpr), and 1,3-bis(5-nitrosalicylideneamino)-2-propanol (H3pnsalpr), were synthesized by reaction of cobalt(II) acetate and Schiff-base ligand in organic solvent. The molecular structures of [CoII{CoIII(µ-HL)(µ-CH3COO)(CH3COO)}2] (HL = Hsalpr and Hbrsalpr), [CoII{CoII(µ-Hsalpr)(µ-CH3COO)}2], and [CoIII2CoII4(µ-L)2(µ3-CH3O)2(µ-CH3O)6(CH3OH)2] (L = salpr, clsalpr, brsalpr, and pnsalpr) were elucidated by the single-crystal X-ray crystallography. The electronic spectra and magnetic susceptibilities with variable-temperature are in accordance with the trinuclear and hexanuclear molecular structures. The cyclic voltammograms are basically featureless, giving a clue to isolate reduced complexes [CoII{CoII(µ-Hsalpr)(µ-CH3COO)}2] and [CoII2(Hpnsalpr)2(CH3OH)]. These findings may guide future design of molecular magnet materials.