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Advance Sustainable Science, Environmental Engineering and Technology (ASSET) Jl. Sidodadi Timur No.24, Karangtempel, Kec. Semarang Tim., Kota Semarang, Jawa Tengah 50232
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Advance Sustainable Science, Engineering and Technology (ASSET)
Published by Universitas PGRI Semarang
ISSN : -     EISSN : 27154211     DOI : https://doi.org/10.26877/asset
Core Subject : Science, Engineering,
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
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. The Scope of ASSET Journal is: Biology and Application Chemistry and Application Mechanical Engineering Physics and Application Information Technology Electrical Engineering Mathematics Pharmacy Statistics
Arjuna Subject : Teknik Kimia (semua kategori) - Teknik Kimia (Lain-Lain)
Articles 305 Documents
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Performance of Self-Compacting Concrete Mixed and Cured with Magnetized Water Alshirah, Majed; Zahid, Mohd Zulham Affandi Bin Mohd; Abu Bakar, Badorul Hisham Bin; Jaafar, Zul Fahmi Bin Mohamed
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2825

Abstract

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.
Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features Vineeta Khemchandani; Alok Singh Chauhan; Shahnaz Fatima; Jalauk Singh Maurya; Abhay Singh Rathaur; Kumar Sharma, Narendra; Daya Shankar Srivastava; Vugar Abdullayev
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2833

Abstract

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.
Stratification and Charging Efficiency in Compact Thermal Storage Under Variable Flow Conditions: An AI-Assisted Simulation Study Simanjuntak, Janter P.; Daryanto, Eka; Tambunan, Bisrul Hapis; Silaban, Robert; Sinaga, Denny Haryanto; Zainon, Mohd Zamri; Muhammad Ibrahim
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2031

Abstract

Thermal Energy Storage (TES) systems are essential for managing low-grade heat in renewable energy applications. This study evaluates the impact of flow rate and heating power on thermal stratification and efficiency within a 30-liter TES unit. Using an AI-assisted simulation framework, the system's performance was analyzed across varying flow rates (0.3–0.9 LPM) and heater capacities (1.5–2.0 kW). Results indicate that lower flow rates (0.3–1.2 LPM) effectively preserve stratification, whereas higher rates induce thermal mixing. While charging efficiency generally decreases as target temperatures rise, it improves significantly with higher heater power. Notably, the configuration using a 0.7 LPM flow rate and 2.0 kW heater achieved a peak efficiency of 78% while maintaining stable thermal layering. This research demonstrates how AI-driven modeling can optimize charging behavior, providing critical insights for the design and thermal management of compact TES systems in low-grade heat applications.
Pectin-based Edible Coatings with Lemongrass Essential Oils for Shelf-life Extension of Papaya Danar Praseptiangga; Husnawati Fatihah Habibie; Dyah Ayu Ashari; Dea Widyaastuti; Rohula Utami; Lia Umi Khasanah
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2043

Abstract

The urgency of biodegradable packaging as an alternative for plastic-based packaging has been demanding. Accordingly, in this study, the development of pectin-based edible coating incorporated with lemongrass essential oils (LEO) has been done to sustain the physicochemical quality of papaya. The edible coating was prepared by using a simple solution method, while LEO was produced by using the water vapor distillation method. Basically, there are five applied treatments, which are uncoating (control), dip-coating (DC), spray-coating (SC), dip-coating with LEO introduction (DC/LEO1%), and spray-coating LEO incorporation (SC/LEO1%). The physicochemical characteristics of papaya were examined over 12 days of storage. Compared to the control, the application of edible coating was significantly (p < 0.05) enhanced weight loss, pH, ascorbic acid, and yeast and mold counts. Therefore, by mixing pectin-based coating solution with LEO can preserve the quality of papaya for almost more than 12 days at room temperature.
Optimized Deep Learning Framework for Clinical Data Classification Using Firefly-Enhanced Stacked Sparse Autoencoders Mudhafar, Yousif Samer; Al-Fatlawy, Ramy Riad; Al-Fatlawy, Ali Ahmed; Shakir, Aboothar Mahmood
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2283

Abstract

Diabetes is a chronic metabolic disorder characterized by sustained high blood sugar levels, which frequently cause complications, including neuropathy and cardiovascular disease. Due to the complex and nonlinear nature of clinical data, accurate and timely prediction is challenging. Traditional approaches struggle to generalize or extract rich features from low-resolution datasets. In this paper, a hybrid deep learning model (FA-SSAE: Firefly Algorithm-based Stacked Sparse Autoencoder) is proposed to improve diabetes classification using the Pima Indians Diabetes dataset. Data is synthesized using Variational Autoencoder (VAE) developed data augmentation and deep features are extracted using SSAE. The model achieved 91.67% accuracy, 96.38% precision, and 98.75% recall; results that significantly outperformed several state-of-the-art methods. The results demonstrate the robustness and reliability of the proposed approach. Its lightweight architecture can be deployed in resource-limited environments, providing value for mobile or embedded systems used in remote clinics. This research advances the development of scalable and accessible tools for diagnostic detection of diabetes in the earliest possible stages to aid in unsupervised clinical care.
Optimizing Cost Performance in Green Industrial Palm Oil Projects Using AI-Based AEC Pahlevi Wijaya, Ferry; Amin, Mawardi
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2353

Abstract

This study investigates green industrial palm oil project cost performance determinants through Artificial Intelligence (AI)-based Architecture, Engineering, and Construction (AEC) systems. The study employed a Structural Equation Modeling Partial Least Squares (SEM-PLS) approach to analyze significant data collected from 115 respondents through 166 validated indicators. Ten primary drivers were identified, and alternative water sources topped the list, followed by indoor air quality auditing, green material, and smart metering systems, all of which were identified as primary cost-effectiveness drivers. Simulations of Green Mark certification levels (Gold, Gold Plus, and Platinum) indicated potential cost savings of 7.01% to 7.05%. The model continued to have very good predictive capability with an R² value of 0.791, testifying to the robustness of the methodology presented. The results validate the engineering value of AI-aided AEC in cost performance maximization and enhancement in eco-friendly industrial building. The findings also offer practical suggestions for design, planning, and execution of cost-saving, eco-friendly palm oil mills.
Analyzing Digital Utility App Adoption: A UTAUT Approach on PLN Mobile with Technological Literacy as a Moderator Adi Susantyo, Ignatius; Yuldinawati, Lia; Apsari Sugiat, Maria
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2367

Abstract

This study examines customers' determinants of behavioral intention to utilize the PLN Mobile application using the Unified Theory of Acceptance and Use of Technology (UTAUT) with technological literacy as a moderating variable. The data were collected from 399 respondents in the UP3 Western Flores Area using purposive sampling and analyzed by Partial Least Squares Structural Equation Modeling (PLS-SEM). The model demonstrated adequate reliability and validity (AVE > 0.5; composite reliability > 0.7) with R² = 0.62 for behavioral intention. Results indicate that performance expectancy, perceived usefulness, social influence, and facilitating conditions significantly influence intention to use the app, β = 0.21–0.34, p < 0.05, while trust and hedonic motivation were not significant. Technological literacy cemented the relationship between intention and real use, emphasizing digital capability as a key adoption driver. Active usage is minimal amid high download rates. The findings provide theoretical contributions to digital service adoption models and practical implications for facilitating user support, literacy programs, and mobile utility system introduction.
Technology Adoption of Utility Mobile Applications across Generational Cohorts Using UTAUT: A PLS-SEM Approach Kustiawan, Isce; Apsari Sugiat, Maria
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2368

Abstract

This study examines the determinants of users’ intention to adopt the PLN Mobile application among Generations X, Y, and Z in East Nusa Tenggara, Indonesia, by extending the Technology Acceptance Model (TAM) with additional constructs, including perceived value, perceived trust, perceived security, attractiveness of alternatives, and social influence, with generational cohort as a moderating variable. A quantitative causal design was applied, collecting data from 438 PLN customers using proportional stratified sampling across four regional offices. Data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results revealed that perceived ease of use (β = 0.322, p < 0.001), social influence (β = 0.268, p < 0.001), and perceived security (β = 0.194, p < 0.01) had significant positive effects on intention to use, while perceived value, perceived trust, and perceived usefulness were not significant predictors. Social influence also significantly influenced perceived trust (β = 0.531, p < 0.001). Moderation analysis indicated that Generation Y exhibited the strongest moderating effects across most relationships, whereas Generation Z had the least impact. These findings provide actionable insights for public digital service providers, emphasizing the need to enhance ease of use, strengthen security, and leverage peer influence to improve adoption across generational segments.
Designing a Human-Centered Smart Counter for Transjakarta Using the House of Quality to Improve Service Inclusivity Pangastuti, Nova; Oktariska Timbayo, Olivia; Pinasthika, Restu
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2405

Abstract

Jakarta's increasing vehicle usage has exacerbated air pollution, and as a result, the initiative to advance sustainable urban mobility and a target to achieve Net Zero Emissions by 2050. Nevertheless, public satisfaction with Transjakarta remains low due to inconsistent service quality and no real-time information for riders. This study puts forward the SmartCounter, a smart passenger-counting system developed by a Human-Centered Design (HCD) process with support from the SERVQUAL approach and House of Quality (HoQ) analysis. The research employs a mixed-method methodology using gap analysis, semi-structured interviews, and focus group discussions to comprehensively gather and convert user requirements into technical specifications. Critical parameters that are elicited from SERVQUAL then propel the Voice of Customer and subsequently get mapped to ranked technical needs with the help of the HoQ. SmartCounter utilizes cutting-edge sensing technology (Time-of-Flight or AI-integrated cameras) with onboard edge computing to enable automatic, real-time, and privacy-respecting passenger counting. The HoQ study prioritized three main technical imperatives: sensor accuracy (score 123), casing robustness (score 111), and real-time transmission (score 109). Other aspects include embedded processors (score 103), display units and operator dashboards (scores 84), and power systems (score 71). Overall, the SmartCounter actively addresses both passenger and operational needs, advancing Jakarta's goals towards a more sustainable, efficient, and inclusive urban transport system for Net Zero Emissions 2050.
Evaluation of Learning Management System for Users with Accessibility Needs Using Extended Technology Acceptance Model (E-TAM) Zatin Niqotaini; Henki Bayu Seta; Theresiawati; Dwi Vernanda; Artika Arista; Muhammad ibrahim Al Farisi; Rapolo Joshua Napitupulu
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2495

Abstract

Inclusive education aims to provide equal learning opportunities for students with special needs, including the use of a Learning Management System (LMS). The urgency of this research stems from the significant challenges in LMS accessibility, which pose major obstacles for students with disabilities. These challenges include difficult navigation, a lack of screen reader features, and unfriendly interface design. The objectives of the research are to identify and evaluate the factors of LMS acceptance by students with disabilities and provide recommendations. The method uses the Extended Technology Acceptance Model (E-TAM) to identify factors influencing the acceptance of LMS by students with disabilities, such as perceived usefulness, perceived ease of use, and external factors. The findings indicate that System Quality (SQ) has no significant influence on Attitude Toward Using (AT), with the estimated effect size being 1.4%. As an implication, the institutions need to provide easy-to-follow guides to help users with disabilities.

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