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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 330 Documents
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Microencapsulation of Phycocyanin from Spirulina platensis by Freeze-Drying: Optimization of Maltodextrin–Soy Protein Matrices for Enhanced Stability and Antioxidant Functionality Marlina, Dian; Saputra, Ryan Werytama; Aji Prasetyo, Takad Bagas; Muhamad Ansory, Hery; Aisiyah, Siti; Purwaningsih, Desi
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2726

Abstract

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.
Comparative Study of Classical and Quantum Machine Learning Models: Insights into Quantum Advantage in Materials Informatics Tri Joko Harjanto, Aris; Dwi Purnomo, Hindriyanto; Hendry
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2733

Abstract

Quantum Machine Learning (QML) has emerged as a promising paradigm for addressing increasing computational and representational demands in materials informatics. While classical models such as Support Vector Machines (SVM) achieve strong predictive performance, they often struggle to capture complex, highly correlated interactions in high-dimensional materials data. QML addresses this challenge by leveraging quantum-mechanical principles to construct expressive feature embeddings, where prospective quantum advantage lies in generating feature spaces that are difficult to approximate classically. In this study, 1,000 crystalline compounds from the Open Quantum Materials Database (OQMD) are evaluated in a binary classification task based on formation-energy stability. The dataset is normalized, reduced to four dimensions via Principal Component Analysis (PCA), and encoded into quantum circuits. Three QML models—QSVM, VQC, and QNN—are benchmarked against a classical SVM using repeated stratified evaluation. Results show that the classical SVM achieves the highest accuracy (91.8% ± 0.012), followed by QSVM (60.8% ± 0.035), while VQC and QNN perform significantly worse. This gap is driven by limited qubit capacity, encoding inefficiencies, restricted circuit expressivity, and optimization challenges. Nevertheless, QSVM demonstrates stable performance, suggesting that potential quantum advantage may emerge from improved feature encoding and kernel design rather than deeper variational circuits.
Microwave-Pyrolysed Rice Husk-Derived Activated Carbon as a Sustainable Anode Material for Lithium-Ion Half-Cell Batteries Surib , Nur Atiqah; Mohd Abdah, Muhammad Amirul Aizat; Farhana Syakirah Ismail; Wong, Weng Pin; Ab Latif, Farah Ezzah; Mustafa, Muhammad Norhaffis; Rashmi Walvekar; Mohammad Khalid
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2740

Abstract

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.
Experimental Analysis of Voltage and Multi-Point Temperature Distribution in a 6S Lithium-Ion Battery Pack Under Constant Current Loading Wildan Louise Fernando; Venugopal Thangavel; Hisyam Ma’mun
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2751

Abstract

Lithium-ion batteries are widely utilized in energy storage applications; however, temperature non-uniformity remains a critical issue affecting performance, safety, and lifespan. This study presents an experimental investigation of the correlation between voltage and multi-point temperature distribution in a 6S lithium-ion battery pack under a constant ±5 A charge–discharge current. Temperature measurements were obtained from three sensor locations to capture spatial thermal variations during operation. The results reveal that the central cell consistently exhibited the highest temperature, reaching approximately 40 °C, while a maximum thermal gradient of 5.7 °C was observed across the pack. Furthermore, a positive correlation between current and temperature indicates uneven heat generation among cells. These findings provide direct experimental evidence of thermal asymmetry in multi-cell configurations and emphasize the importance of optimized sensor placement and enhanced thermal management strategies in Battery Management Systems (BMS).
Optimization of Mangrove Glucomannan Addition to Improve Physicochemical Properties of Kefir Jeki Mediantari Wahyu Wibawanti; Zulfanita; Dita Yuzianah; Harisun Binti Ya’acob; Anang Mohamad Legowo; Setya Budi Muhammad Abduh; Sri Mulyani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2756

Abstract

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. 
A Hybrid AI–SEMPLS Model for Digital Visualization Acceptance in Blue Tourism: Evidence from Lampung Province Alita, Debby; Nisa, Khoirin; Styawati; Amelia, Dina
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2909

Abstract

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.
Supply Chain Performance Evaluation in the Pulley Manufacturing Industry Using Supply Chain Operations Reference and Analytical Hierarchy Process Qurtubi; Haswika; Sangkhiew, Noppakun; Kusrini, Elisa
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3104

Abstract

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).
Hybrid XGBoost-LSTM Framework for Accurate SOC, SOH, DOD and Internal Resistance Estimation in Li-ion Cells Putra Pralano, Axel; Florence Gnana Poovathy John; Rifki Hermana
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3119

Abstract

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.
A Decision Support System Based on Transformer-Driven Sentiment Analysis of Social Media Data Wibisono, Arie Christian; Princes, Elfindah
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3123

Abstract

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.
Bottleneck Analysis and Improvement in Apparel Manufacturing Production Processes Using Integration Design of Experiments and Discrete Event Simulation Muchtar, Diki; Moengin, Parwadi; Surjasa, Dadang; Cahyati, Sally
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3288

Abstract

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.

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