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 Science and Technology Research Centre 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
Implementation of The Green Campus Concept Based on The UI GreenMetric Guidelines Approach
Climate change and rapid urban development have increased the urgency of sustainable practices in higher education institutions. Green areas on campuses play a vital role in reducing carbon emissions, improving microclimate, and supporting ecological balance. This study analyzes the implementation of green area criteria based on the UI GreenMetric World University Rankings 2023 guidelines. A descriptive-analytical method was employed, combining field observations, documentation review, and interviews with campus facilities management. The Green Campus assessment results based on the UI GreenMetric guidelines, UMB is ranked 72nd among universities in Indonesia and 756th in the world based on the UI GreenMetric Ranking 2020. These results suggest that while the campus demonstrates strong compliance with the UI GreenMetric benchmarks, further improvement in conservation and community engagement is necessary.
Copyright (c) 2025 Advance Sustainable Science Engineering and Technology
Systemic Model of Driver Fatigue on Extreme Routes: PLS-SEM Analysis of Supervisor Support and Organizational Justice
Driver fatigue is a critical safety concern for long-distance bus operations, particularly on the extreme route of Bima–Mataram. The study examines the impact of supervisor support and perceived penalty fairness on drivers' compliance with rest periods and levels of fatigue. Data from 114 drivers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance–Performance Map Analysis (IPMA). The results indicate that supervisor support positively affects rest compliance (β = 0.38), which in turn decreases fatigue (β = –0.35); penalty fairness has a negative effect on fatigue (β = –0.29) directly. Accordingly, IPMA provides evidence that supervisor monitoring and penalty system consistency are high-impact yet underperforming priorities. These findings reveal that fatigue acts as a systemic variable developed by organizational and policy factors. The implications point out the necessity of improving supervisory capacity, penalty system reform to ensure fairness and transparency, and the integration of fatigue detection technologies to enhance safety interventions on high-risk routes.
Optimization of Saccharomyces cerevisiae Dose in Eco-Enzyme Fermentation: Effects on pH, BOD, DO, Nitrite, and Nitrate
The contribution of Saccharomyces cerevisiae is expected to accelerate the fermentation process and enhance the microbial population involved in eco-enzyme production. This study aimed to analyze the potential role of S.cerevisiae in improving eco-enzyme production. A true experimental method was employed using a completely randomized design with four treatments and three replications. The results, based on the average matrix similarity test using the Wilks’ Lambda Test, showed that the significance values for pH, DO, BOD, nitrite, and nitrate were < 0.05. Therefore, H0 was rejected, indicating that varying doses of S.cerevisiae had a significant effect. Furthermore, significant differences were observed among the treatments for each dependent variable. In conclusion, S.cerevisiae contributes to enhancing the overall quality of eco-enzymes by facilitating fermentation and accelerating the decomposition of organic matter.
Physics-Informed Neural Network with Thevenin Equivalent Circuit for Accurate SOC Li-ion Battery Estimation
Accurate state of charge (SOC) estimation is essential for the safety, performance, and longevity of lithium-ion batteries. Physics-based models such as equivalent circuit models (ECMs) are computationally efficient but struggle under nonlinear and time-varying conditions, whereas purely data-driven approaches often lack interpretability. This study proposes a hybrid framework that integrates a physics-informed neural network (PINN) with a first-order Thevenin ECM for dynamic SOC estimation using only terminal voltage and current inputs. The method incorporates physically derived parameters including open-circuit voltage (OCV), polarization resistance, and capacitance identified through pulse testing. An eighth-order OCV–SOC polynomial regression optimized with a genetic algorithm (GA) enables nonlinear mapping, while the Newton–Raphson (NR) method is applied for final SOC estimation. Experimental validation was performed on 18 Ah lithium iron phosphate (LFP) cells over 300 charge–discharge cycles at 20 °C, extended up to 2000 cycles under 1C/2C rates with cut-off voltages of 3.7 V and 2.7 V. Comparative analysis with extended kalman filters (EKF) and standard neural networks (NN) demonstrates the superiority of the proposed method, achieving a root mean squared error (RMSE) of 0.103, mean absolute percentage error (MAPE) of 0.702%, and coefficient of determination (R²) of 0.998. By embedding physical constraints into the learning process, the PINN enhances accuracy, robustness, and generalizability, while reducing estimation uncertainty, thereby offering a scalable and interpretable solution for real-time battery management systems (BMS) in electric vehicles (EVs) and battery energy storage systems (BESS).
AI-Enabled CTAS and Digital Tax-Fraud Detection: A PLS-SEM Study in Indonesia
This study investigates the factors determining digital tax fraud based on the New Fraud Star Theory, with great emphasis on the moderating role of AI-empowered CTAS. Data were collected from 107 corporate taxpayers in Indonesia through a structured survey and analyzed using Partial Least Squares Structural Equation Modeling. The results indicated that System Pressure, Technological Capability, and External Digital Pressure significantly heightened fraud attempts, while Digital Opportunity, AI Rationalization, Cyber Arrogance, Internal IT Governance, and Techno-Culture were not significant. The model explained a substantial variance in the effectiveness of fraud detection with R² = 0.723. Moderation analysis showed that AI-powered CTAS significantly weakened the effects of System Pressure (X1×CTAS), Technological Capability (X4×CTAS), Internal IT Governance (X6×CTAS), and External Digital Pressure (X7×CTAS). These findings identify CTAS's strategic role in improving compliance by enabling real-time data integration, anomaly detection rules, and strengthened access control. Implications are that digital governance reforms should give full attention to the establishment of robust AI-empowered monitoring systems to minimize the risk of tax fraud effectively.
Adaptation and Validation of the Indonesian Version of R.I.G.H.T. Leadership Scale
Leadership has a significant impact on organizational resilience and employee well-being. In accordance with International Test Commission guidelines, this study translated and validated the R.I.G.H.T. Leadership Scale for Indonesian employees. Online information was gathered from 302 workers from various industries (162 men and 140 women, ages 19–41). Psychometric testing, expert review, and forward-backward translation were all part of the adaptation. With over 90% expert agreement, the content validity was excellent (I-CVI and S-CVI = 0.99). A good model fit was found by confirmatory factor analysis (χ² = 136.65, df = 80, p <.001; RMSEA = 0.036; CFI = 1.00; GFI = 0.94). With slight declines ascribed to cultural and linguistic factors, reliability was high (α = 0.804–0.884). Despite its limitations, which include young samples and a lack of test-retest data, the Indonesian version is generally valid, dependable, and helpful for evaluating leadership practices.
Empirical Analysis of the Impact of Labor Coefficients on Column Reinforcement Productivity in Construction Projects
Construction productivity, particularly in column reinforcement, is significantly influenced by labor as a key project component. Variations in labor coefficients determine efficiency in time, cost, and work quality, necessitating empirical analysis of their impact on productivity. This study examines the relationship between labor coefficients and column reinforcement productivity to improve construction project management efficiency. Using a quantitative approach with purposive sampling, 33 observation data were collected through field measurements and questionnaires from workers and foremen. Simple linear regression was applied to test labor coefficient significance, with results compared against PUPR Ministerial Regulation No. 8 of 2023 standards. Analysis revealed that field labor coefficients significantly affect column reinforcement productivity (p < 0.001), demonstrating that optimal labor utilization increases productivity. The comparison with ministerial standards evaluated field condition conformity with official provisions. The research hypothesis confirming significant influence between field labor coefficients and column reinforcement productivity was accepted, providing valuable insights for construction management practices.
Hybrid Expert System for Academic Stress Diagnosis Using Forward Chaining and Score Weighting
Academic stress classification is a significant challenge in education, as previous approaches often rely on opaque models or require large training datasets. This study develops a hybrid expert system for academic stress classification using forward chaining and Certainty Factor (CF) score fallback. The system was tested on 100 student cases with the following label distributions: Mild (48), Moderate (37), and High (13), classified independently by three experts. Label validity was tested using pairwise Cohen's kappa, yielding a mean value of 0.8280. The system achieved 100% accuracy, a 32% improvement over the classical forward chaining baseline (68%). Statistical evaluation using Wilson score intervals demonstrated high consistency across all key metrics (accuracy, precision, recall, F1-score) with a 95% CI of [96.4%, 100%]. The system is designed with an explicit and auditable rule structure, enabling deterministic classification based on symptoms. Although validation results are high, the unbalanced label distribution opens up the potential for spectrum bias. Going forward, the system is planned to be tested across institutions, assessed for integration with counseling services, and compared with other hybrid approaches.
Smart Hospitality and Innovation: The Role of Leadership, Local Wisdom, and Technology Integration in Five-Star Hotels
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.
Development of A Parametric Cost Estimation Model for Landfill Construction Projects
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
Barium Removal from Produced Water Using RCC-Based Ceramic Adsorbent: Fixed-Bed Column Adsorption
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.
“Demata 2.0”: An On-Device AI Assistive Technology for the Visually Impaired Integrating YOLOv10 and OCR
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.
Assensing the Impact of Advanced Driver Assistance Systems (ADAS) on Road Safety an Empirical Study Using Factor Analysis
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.
Quantifying the Causal Impact of Employment Trends on Academic Performance Using Time-Series and Public Interest Data in Indonesia
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.
Evaluating Ordinal Multivariate Models under Multicollinearity via Pairwise Likelihood: A Simulation Perspective
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.
High-Resolution Smart Card-Based OD Matrix for Optimizing Jakarta’s LRT Operations
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.
Predicting Habitat Suitability of Mahseer Fish (Tor spp.) in Tropical River Systems Using MaxEnt and Google Earth Engine: A Geospatial Modeling Approach
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.
Biosorption of Chromiun in Batik Wastewater Using SCOBY Microbial Biomass: A Sustainable Bioremediation Approach
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.
Development of an IoT-based Soil Nutrient Monitoring and GIS Mapping System for Precision Agriculture
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 to 36.80 degrees. Soil moisture varied from 4% to 89.10%. The soil pH varied between 6.90 and 8.07. 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.
Determinants of MSME Sustainability: A Regression-Based Study in Labuhanbatu
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.
Comparative Analysis of Observed and Empirical Rainfall Distribution for Flood Hydrograph Modeling
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.
An Artificial Neural Network Approach for Predicting Pavement Distress: A Case Study Toward Sustainable Road Maintenance
The Surface Distress Index (SDI) is a crucial parameter to consider when determining road conditions as part of an effective maintenance strategy. This study aims to develop an SDI prediction model using road surface distress data to enhance maintenance planning. The developed Artificial Neural Network (ANN) model resulted in an optimal structure with two hidden layers comprising 6 neurons and 4 neurons, respectively. The model was trained using two years of surface distress data collected from 40 road sections managed by the city’s road maintenance division. Variables used included Composition, Condition, Depression, Patches, Damage types, Crack Area, and Crack Width. The results demonstrated high accuracy in predicting SDI, with model performance achieving an R² of 0.87. This model can be applied to optimize the efficiency of road maintenance strategies.
Evaluating the Role of Extractives in Biomass Pyrolysis for Enhanced Hydrogen Syngas Production
This study explores how extractive content in lignocellulosic biomass affects syngas quality during fixed-bed pyrolysis-gasification, specifically focusing on hydrogen (H₂) concentration. While woody biomass is a known energy source, the link between its non-structural organic compounds (extractives) and H₂ in syngas is often overlooked. We investigated teak, coconut, and jackfruit wood to understand this influence and optimize temperature for better biomass-to-hydrogen conversion. An MQ-8 sensor detected H₂ levels. Results show that biomass with high extractive content significantly boosts syngas H₂. Jackfruit wood yielded the highest H₂ concentration (2898 ppm at 471°C), outperforming coconut wood (1965 ppm at 444°C) by 41.7% and teak wood (1931 ppm at 395°C) by 50.1%. This is due to jackfruit's high cellulose and extractive content, which decompose efficiently at higher temperatures. Overall, high-extractive biomass improves syngas quality and expands sustainable options for hydrogen production.
Boosting-Based Machine Learning Models and Hyperparameter Tuning for Predicting Vehicle Carbon Dioxide Emission
Sustainable development and climate change are central agendas in global policy and research. This study examines and compares three ensemble learning models using Gradient Boosting Machine, Categorical Boosting, and Extreme Gradient Boosting for forecasting vehicle carbon dioxide (CO2) emissions. Data preprocessing with Interquartile Range (IQR) and median imputation is among the methods used to address missing values in CO₂ rating and smog rating variables. SHAP and PDP were employed for feature importance analysis and model interpretability. The findings from the third experiment demonstrate that Extreme Gradient Boosting (XGBoost) outperformed other models achieving a Coefficient Determination of 0.9988, Root-Mean-Square Error of 2.1696, Mean-Absolute Error of 0.4977, and Mean-Absolute-Percentage Error of 0.0019. The primary predictive features included combined fuel consumption (liters/100 km), city and highway fuel consumption, ethanol fuel consumption, model year, engine size and diesel consumption. The findings suggest the potential of boosting-based models for supporting sustainable transport planning, policy for emission reduction, and evidence-based policy making.
Implementing Lean Manufacturing Using Value Stream Mapping for Automotive Maintenance Efficiency
Transportation is very vital to society these days as it provides mobility and maintains economic growth. In January 2022, Indonesia was Southeast Asia's largest auto market. PT Blue Bird Tbk., one of the largest car rental and transportation companies in Indonesia, faced very critical problems involving workshop efficiency and customer satisfaction. This study will optimize the car repair process via the implementation of Lean Manufacturing principles with the Digital Value Stream Mapping (DVSM) technique. Identification of waste was conducted via Waste Assessment Model (WAM) and a directed questionnaire, wherein the three major types of waste were overproduction (21.07%), defects (16.89%), and waiting (16.65%). For these, two VALSAT tools—Process Activity Mapping and Supply Chain Response Matrix—were employed. Simulation using Arena software was performed to validate the proposed improvements. Upon implementation, the lead time for car repair decreased from 12.887 seconds to 12.203 seconds, a decrease of 5.3%. The research indicates that lean tools can be effectively applied in car maintenance services, particularly in developing countries, to increase operating efficiency and service performance.
Dual-Chamber Microbial Fuel Cell for Bioelectricity Generation Using Coastal Sediments: A Case from Kendari Bay
This study evaluates the potential of Kendari Bay sediment as an alternative source of electrical energy through the dual-chamber Sediment Microbial Fuel Cell (SMFC) technology. The research focused on sediment characterization, performance analysis using an aerator and KMnO₄, post-operation substrate changes, and the identification of electrogenic bacteria. The results showed that the sediment contained 43.24% moisture, 4.23% organic carbon, 1.08% total nitrogen, a C/N ratio of 3.92, pH 7.38, and conductivity of 11.56 mS. The SMFC generated a voltage of 0.404 V (aerator) and 1.628 V (KMnO₄), along with a current of 5.0 µA. After SMFC operation, organic content decreased, with 42.65% moisture, 4.06% organic carbon, 0.97% total nitrogen, a C/N ratio of 4.19, pH 7.86, and conductivity of 15.78 mS. Identified bacteria were Gram-positive Bacillus spp. These findings demonstrate that aerator and KMnO₄ application in dual-chamber SMFC significantly enhance energy conversion efficiency using marine sediment.
Kendari Bay Sediment, bioeletricity, SMFC
Optimizing Human Resource Performance in Building Construction through Technology-Enhanced Strategy Development
Construction projects involve complex processes requiring effective management and skilled human resources to ensure successful outcomes. This study analyzes key factors influencing human resource (HR) performance in building construction, identifying ability, working conditions, organizational structure, motivation, discipline, and compensation as critical determinants. A structured questionnaire using a 5-point Likert scale was employed as the data collection instrument, distributed to 130 contractors, with 114 valid responses collected in Semarang, Indonesia. Data analysis using SPSS v.27 confirmed that all indicators are valid, reliable, and positively perceived, with “ability” receiving the highest rating (mean = 4.8). Practical implications for project stakeholders include the need to implement targeted training, performance-based incentives, leadership development, and optimized recruitment. Technological integration is also emphasized for enhancing communication and decision-making efficiency. The findings underscore the importance of a comprehensive HR strategy that addresses both individual competencies and systemic organizational support, advancing sustainable engineering practices and improving project productivity in dynamic construction environments.
Enhancing Energy Efficiency in Industrial Warehouses Using Life Cycle Cost Analysis (LCCA)
Industrial warehouses use inefficient lighting systems that occupy a significant portion of energy consumption and operating expenses. The aim of this study is to attain higher energy efficiency and reduce the lighting cost by replacing conventional high-pressure mercury vapor (HPL-N) lamps with LEDs in a warehouse environment. The study employs the Life Cycle Cost Analysis (LCCA) methodology to evaluate long-term cost-effectiveness and save energy. Data were collected using literature review, field surveys, and light simulation using Dialux software at PT XYZ warehouse in Cilegon, Indonesia, between January and June 2024. Findings reveal that the use of LED lighting, in conjunction with the optimization of the light point number, reduces energy consumption by 65%, saving 121,929.816 kWh of energy annually. Within 11 years, installation of LED saves an amount of Rp 947,683,204 compared to Rp 1,796,422,585 with HPL-N systems—an amount of 50.66% or Rp 848,739,381 saved. The results show the energy and economic benefits of the use of LED in industrial warehouses to be advantageous for national energy saving as well as satisfying light standards.
Secure Visual Image Encryption Using Lorenz Chaos, Steganography, and Wavelet-Based Steganography Authentication
Medical images play a vital role in diagnosis and clinical decision-making, yet their transmission and storage pose significant privacy and security challenges. This research proposes a visual stego-encryption system that integrates the Lorenz chaotic algorithm with Discrete Wavelet Transform (DWT) to embed both secret medical data and a doctor's digital signature into a visually meaningful encrypted image (VMEI). The system employs dual-layer embedding and role-based access control, allowing administrators to input patient and doctor data while enabling doctors to perform secure validation and decryption. A series of evaluation scenarios were conducted, including variations in image resolution, geometric transformations (rotation), Lorenz initial conditions, alpha embedding parameters, and multivariate optimization, along with user-role-based validation testing. Performance metrics based on Peak Signal-to-Noise Ratio (PSNR) and Bit Error Rate (BER) demonstrate that the system consistently achieves high visual fidelity (PSNR > 30 dB) and low data loss (BER ≈ 0) across all image types. The optimal configuration—using a 4096×4096 carrier, 1024×1024 secret, and 256×256 signature with α₁ = 0.28, α₂ = 0.07, and initial condition (0.2, 0.8, 1.5)—resulted in a PSNR of 33.01 dB for the secret image. These results confirm that the proposed system provides a robust, secure, and visually accurate method for medical image encryption, suitable for integration into real-world digital healthcare infrastructures.
Simulation-Based Optimization of Resource Allocation in Seasonal Recreational Facilities Using Discrete Event Simulation and Machine Learning
The study proposes a simulation-based optimization framework to surmount recreational facility operational inefficiencies via spatial design, guest flow, and staff allocation. Adopting Discrete Event Simulation (DES) and Machine Learning (ML), the research optimizes capacity planning and resource allocation in the face of dynamic seasonal demands. A year's worth of operations data was utilized for statistical distribution modeling of visitor interarrival times in RStudio, categorized into low, regular, and high seasons. The simulation model, developed in AnyLogic, uncovered service bottlenecks—particularly at ticketing counters and photo points. Validation results indicated close alignment with real-world operational metrics, ensuring model validity. Actionable suggestions are provided in terms of dynamic employee scheduling and spatial reconfiguration for improved efficiency and visitor experience. By integrating DES and ML, the study contributes to sustainable operations and provides a transferable method for the optimization of service systems in weather-dependent recreational environments.