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
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.
Copyright (c) 2026 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
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
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).
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.
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 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.
Comparative Evaluation of Automatic Labeling and Modeling Strategies for Indonesian Sentiment Analysis: Metodology and Performance Evaluation
Sentiment analysis is vital for understanding consumer perception, yet Indonesian sentiment classification faces challenges due to labeled data scarcity and computational constraints. This study advances automatic labeling techniques and establishes performance benchmarks for Indonesian text. The research compares two labeling approaches InSet Lexicon and IndoBERT based Hugging Face pipeline on 8,447 Tapera-related opinions. Results show InSet Lexicon produced a highly skewed distribution (89.66% neutral), while the IndoBERT pipeline achieved a more balanced distribution (47.66% neutral, 38.43% positive, 13.91% negative).. Evaluation of various modeling strategies revealed that combining InSet Lexicon + TF-IDF with Naïve Bayes or Random Forest achieved scores above 85%. While RNN-LSTM reached >90% accuracy, it required significant resources. Notably, fine-tuning IndoBERT with optimal hyperparameters yielded the most robust performance, achieving 80–90% accuracy with a low validation loss of 0.1. The study concludes that for small datasets (<12,000 samples), the most effective strategies for Indonesian sentiment analysis are either the InSet Lexicon paired with traditional Machine Learning or automatic labeling using pre-trained models followed by rigorous fine-tuning.
Copyright (c) 2024 Advance Sustainable Science Engineering and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features
Cognitive workload plays a vital role in tasks that demand dynamic decision-making, especially under high-risk and time-sensitive conditions. An excessive workload can lead to unexpected and disproportionate risks, whereas insufficient workload may cause disengagement, undermining task performance. This underscores the importance of maintaining an optimal level of mental focus in high-pressure situations to ensure successful task execution. This study leverages deep learning methods alongside functional connectivity measures to classify cognitive workload levels. Using the N-back EEG dataset, functional connectivity metrics such as Phase Locking Value (PLV), Phase Lagging Index (PLI), and Coherency are extracted after data pre-processing. These metrics, characterized as directed or non-directed, enable efficient computational analysis. A convolutional neural network (CNN) classifier is employed to categorize cognitive workload into three levels: low (0-back), medium (2-back), and high (3-back). The CNN-A architecture achieves peak performance with an accuracy of 93.75% using PLV, 87.5% using Coherency, and 68.75% using PLI.
Performance of Self-Compacting Concrete Mixed and Cured with Magnetized Water
This study investigates the effect of magnetized water (MW) on the mechanical and microstructural properties of self-compacting concrete (SCC). Four mixes with identical proportions were prepared to isolate the influence of MW at different stages: SCNTN (normal water used in both mixing and curing), SCNTM (normal water in mixing and MW in curing), SCMTN (MW in mixing and normal water in curing), and SCMTM (MW used in both mixing and curing). Compressive strength tests were conducted at 7 and 28 days, and microstructural characterization was performed using X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR). The results showed that using MW during mixing enhances early-age strength through improved dispersion and hydration of cement particles, while using MW during curing contributes more significantly to long-term hydration. The combined use of MW in both mixing and curing (SCMTM) achieved the highest strength values, with improvements of 25.8% at 7 days and 14.2% at 28 days compared to the control mix. Microstructural findings confirmed denser calcium–silicate–hydrate (C–S–H) gel formation and reduced unhydrated phases in MW-treated concrete. These results indicate that MW has a positive influence on both hydration and strength development in SCC.
The Effect of Lean Management on Innovation Performance: How Absorptive Capacity Affects Food Manufacturing Industry?
This study analyzes the effect of Lean Management Practices (LMP) on Innovation Performance (IP) through the mediating role of Absorptive Capacity (AC) in Indonesia’s food and beverage industry. A structured questionnaire adapted from validated instruments (Gaspersz, 2007; Cohen & Levinthal, 1990; Damanpour, 1991) was distributed to 200 managers and supervisors from food manufacturing firms listed in the Indonesian Ministry of Industry registry, yielding 180 valid responses (90% response rate). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0, assessing reliability, convergent, and discriminant validity (HTMT ≤ 0.90). The results indicate that Lean Management strongly influences Absorptive Capacity (β = 0.919, p < 0.001) and both directly and indirectly enhances Innovation Performance (β = 0.690 and 0.725, respectively). Effect sizes (f² = 0.482 – 5.431) and R² > 0.83 confirm the model’s high explanatory power, while confidence intervals (95%) validate the path significance. These findings demonstrate that lean implementation enhances innovation effectiveness primarily through knowledge assimilation and transformation. The study’s main limitation is its single-sector, self-reported cross-sectional design, which may introduce common-method bias. Future research should apply multi-method or longitudinal approaches to increase generalizability.
Optimization of Mangrove Glucomannan Addition to Improve Physicochemical Properties of Kefir
Kefir is widely recognized as a functional fermented dairy product. However, its physicochemical stability, particularly pH, acidity, and syneresis, remains a challenge during processing and storage. The addition of functional polysaccharides, such as mangrove-derived glucomannan, has been proposed to improve kefir quality. This study aimed to investigate the effect of mangrove-derived glucomannan on the physicochemical properties of kefir. A Completely Randomized Design with different concentrations of mangrove glucomannan (0, 2, 4, 6, and 8% (v/v)) was applied, with four replications. The results showed that increasing glucomannan had a significant effect (P < 0.05) on total bacteria, pH, and titratable acidity. Mangrove-derived glucomannan significantly enhanced total bacterial counts in goat milk kefir (p < 0.05), increasing from 6.09 Log CFU/mL in the control to 7.45-7.80 Log CFU/mL at 2-8% concentrations. The treatments decreased pH from 3.66 (0%) to 3.35 (8%) (P < 0.05), while titratable acidity increased from 0.70% (0%) to 0.83-0.85% in the treatment groups (P < 0.05), confirming enhanced fermentation activity. Syneresis decreased at 2% glucomannan (0.93%) but increased slightly at higher concentrations, reaching 1.03% at 8% (P > 0.05). These findings indicate that glucomannan modulates kefir fermentation, as reflected in lower pH and higher acidity values.
Comparative Evaluation of Parameter-Efficient Fine-Tuning Strategies for Continual Image Classification
Catastrophic forgetting remains a major challenge in continual transfer learning, where performance on earlier tasks degrades after sequential adaptation. While full fine-tuning updates all parameters and achieves strong performance on new tasks, it is computationally expensive and prone to forgetting. This study compares parameter-efficient fine-tuning (PEFT) methods—adapters, additive learning, side-tuning, LoRA, and zero-initialized layers—against full fine-tuning on CIFAR-100 using a two-stage protocol: task-A (classes 0–49) followed by task-B (classes 50–99), evaluated on ResNet-18 and ResNet-50. Results are reported as mean ± standard deviation over three runs (n = 3), with retention measured using a Swapback-based recall method that distinguishes true forgetting (Δ). Across both architectures, all PEFT methods maintain task-A knowledge (Δ = 0.00), while full fine-tuning exhibits forgetting (Δ = 0.31 on ResNet-18; Δ = 0.20 on ResNet-50). PEFT methods achieve competitive task-B performance while updating only 0.22–4.49% of parameters. Notably, LoRA on ResNet-50 achieves the highest task-B accuracy (0.82) with only 0.93% parameter updates and no forgetting, slightly outperforming full fine-tuning (0.81). These findings highlight PEFT as an efficient and stable alternative for scalable continual transfer learning.
A Hybrid Deep-Learning and Evolutionary Feature-Selection Framework for Skin Lesion Classification: Application to Monkeypox Detection
The recent resurgence of Monkeypox has highlighted the urgent need for fast and accurate diagnostic tools. In this paper, we propose a new framework of hybrid deep learning to combine both DenseNet121 and MobileNetV2 to obtain both rich and supplementary attributes of the skin lesion images. By pooling the outputs of these two models in terms of features, we get the lightweight representation of the images as well as rich representations of the images. To improve the feature set, we use Genetic Algorithm (GA) which is useful in reducing the dimensions and eliminating redundancy. Optimized features are then categorized with the help of the Random Forest model, which has been selected due to its good performance and capacity to work with high-dimensional data. Using two publicly accessible datasets, MSID and MSLD, we tested our approach and obtained remarkable classification accuracies of 92.71% and 97.77%, respectively. These findings highlight the success of combining ensemble learning, evolutionary optimization, and deep learning to achieve accuracy and proper diagnosis of monkeypox through medical images.
Digital Lean-Process Framework for Creative-Industry Efficiency (Case: Sipirok, Indonesia)
This study develops a community empowerment model for the creative economy by integrating local wisdom, education, institutional support, and technology while applying Value Stream Mapping (VSM) to reduce process inefficiencies affecting sustainable tourism. The research introduces Lean-based process optimization using engineering metrics such as cycle time, uptime, and defect ratio. Results indicate that Lean interventions reduced cycle time by 25.3%, increased uptime by 6.8%, and decreased waiting waste by 21.4%, reflecting significant operational improvements. A mixed-method approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and VSM was employed. From 110 creative industry actors, 86 respondents were selected using the Slovin formula. The findings reveal that education and skills (β = 0.336), institutional support (β = 0.352), and local wisdom (β = 0.249) significantly improve product innovation, which subsequently enhances income (β = 0.409) and sustainable tourism (β = 0.579). Technology showed no significant effect (β = 0.130; p = 0.413). The study demonstrates that combining socio-cultural empowerment with Lean-driven process redesign can strengthen innovation, operational performance, and sustainability in rural creative economies.
Analysis and Design Android Augmented Reality Platform (Bilingual) for the Preservation of Cirebon Glass Paintings
This study analyzes and designs a bilingual Android Augmented Reality (AR) platform to support the digital preservation of Cirebon Glass Paintings. The development uses Unity and AR Core with a human-centered design approach. A total of 30 participants (n=30) evaluated usability and performance. 3D assets were produced using photogrammetry with an optimized polygon budget of ≤25,000 triangles per object. Model compression applied Draco and KTX2 to reduce memory load. Benchmark testing was conducted on Snapdragon 720G class devices. Experimental results show that the platform achieved a stable performance of ≥30 FPS (mean = 32.6 FPS) and low tracking error (RMSE = 1.8–2.3 cm) under indoor lighting. Usability testing yielded a mean System Usability Scale (SUS) score of 81.4 ± 6.2, indicating excellent user acceptance. Compared with existing AR heritage applications, this research provides a reproducible pipeline for AR-based cultural digitization with performance guarantees on mid-range smartphones. The findings imply that optimized AR asset workflows can enhance public interaction with intangible cultural heritage such as Cirebon Glass Paintings. Limitations include restricted device testing and the need for more complex ecotourism content integration in future development.
Unifying Dual-Pyramid Structure and Y–G Channel Synergy for Full-Reference Image Quality Assessment
Standard metrics such as SSIM often overlook complex chromatic distortions, creating a gap between objective scores and human judgment. To address this, we present the Synergistic Structural Similarity Index (SSSI), a metric grounded in a novel dual-pyramid strategy that integrates Gaussian-blurred stability with direct subsampling sharpness. Our method departs from luminance-only analysis by employing an equal, synergistic partnership between the luminance (Y) and Green (G) channels, mirroring the eye's spectral sensitivity. On the KADID-10k dataset, SSSI achieves an SROCC of 0.7793. This represents a significant 4% performance gain over the standard SSIM baseline, demonstrating that integrating chromatic data with dual-scale structural analysis provides a more accurate proxy for human visual perception.
Altitude-dependent Variation in Antibacterial Properties of Red Ginger (Zingiber officinale var. rubrum): Implications for Natural Anti-Salmonella Agents
Red ginger contains diverse bioactive compounds with strong antioxidant and antibacterial activities. This study investigated the influence of growth location on the anti-Salmonella activity of red ginger extracts from seven regions in Java, Indonesia. The fractions were analyzed for total phenolic and flavonoid contents, as well as antioxidant capacity using DPPH and FRAP assays. Antibacterial activity was assessed against Salmonella using the Kirby-Bauer disk diffusion method. Results showed that methanol and ethyl acetate fractions exhibited the highest phenolic and flavonoid contents, while the chloroform fraction demonstrated the strongest radical scavenging activity. Extracts from Bumiaji and Lendah displayed the most potent anti-Salmonella activity (inhibition zone: 10.08 to 18.00 mm). These findings highlight that altitude and solvent polarity influence red ginger bioactivity, supporting its potential as a natural antibacterial source.
Optimization of Image Compression Using K-Means Clustering for Digital Heritage Archives
Preserving digital cultural assets requires efficient compression to minimize storage and bandwidth costs. However, existing studies rarely evaluate K-Means Clustering on structurally complex objects such as the Prambanan Temple, leaving a research gap in assessing its performance against standard codecs. This study introduces a novel optimized K-Means pipeline with adaptive cluster selection and improved centroid initialization for compressing high-detail temple imagery. The method groups pixels based on color proximity, reducing redundancy while preserving key structural patterns. Experiments show that K-Means achieves PSNR 28.08–30.65 dB and SSIM 0.86–0.92, outperforming baseline JPEG at similar file sizes PSNR 26–28 dB, SSIM 0.80–0.87. This quantitative comparison demonstrates the model’s superior perceptual retention in textured stone regions. The methodological contribution lies in combining spatial–chromatic feature weighting with iterative centroid refinement, which increases cluster stability and reduces quantization artifacts. Findings confirm K-Means as a viable alternative for controlled-distortion compression. In conclusion, the proposed approach provides practical engineering implications, enabling reduced storage footprints, predictable reconstruction quality, and integration into hybrid compression pipelines for large-scale digital imaging systems.