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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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INDONESIA
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 352 Documents
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Technology-Driven Digital Marketing Strategies and their Impact on E-commerce Purchase Decisions Devi Setyowati; Maria Apsari Sugiat
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2502

Abstract

The rapid growth of internet penetration and digital platforms has transformed marketing practices in Indonesian Vocational Training Centers (LPKs). However, inconsistent outcomes in trainee recruitment highlight a gap in understanding the effectiveness of digital marketing in this context. This study aims to examine the effect of digital marketing on purchase intention, with consumer engagement as a mediator and consumer trust as a moderator. Data were collected from 384 trainees in East and West Java using online surveys and analyzed with Structural Equation Modeling–Partial Least Squares (SEM-PLS 4.0). The structural model explained 62.1% of the variance in purchase intention (R² = 0.621). Digital marketing significantly influenced purchase intention (β = 0.455, p < 0.001), and consumer engagement partially mediated this relationship (β = 0.217, p < 0.001). In contrast, consumer trust did not moderate the relationship (β = 0.054, p = 0.055). These findings advance digital marketing system development in the education sector by highlighting the role of engagement-driven strategies over trust-building mechanisms during initial recruitment stages. The implications are aligned with Sustainable Development Goal (SDG) 9 on industry, innovation, and infrastructure, suggesting that localized and technology-driven approaches are essential for strengthening LPK competitiveness and sustainable labor mobility.
Performance Optimization of a Petrochemical Cooling Tower via Fill Replacement: Cleanflow vs Cleanflow Plus Demas Ahmad Resha Putra Hidayat; Berkah Fajar Tamtomo Kiono; Sri Widodo Agung Suedy
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2574

Abstract

This study investigates the performance improvement of an induced draft, counterflow cooling tower after replacing the existing Cleanflow fill with Cleanflow Plus. One cell (E) was upgraded while four cells (A–D) served as the baseline under CTI ATC-105 procedures. Measurements included outlet temperature, wet-bulb temperature, circulation flow, and fan power. Results show that Cell E achieved a higher cooling range (9.20°C vs. 8.63°C average) and a lower approach (6.29°C vs. 6.87°C average). Heat-transfer capacity increased from 37.12 MW average to 39.60 MW (+6.68%). Tower capability improved from 89.00% average to 94.47% (+5.47% absolute, +6.1% relative). Number of Transfer Units (NTU) increased significantly from 2.341 to 2.889 units (+23.4%), and effectiveness improved from 71.5% to 75.9% (+6.1%). Evaporation increased from 1.21% to 1.29% (6.6%), while electrical fan power was 149.65 kW (+2.4% relative to baseline). These enhancements are attributed to the higher specific surface area (140.7 m²/m³ vs. 127.0 m²/m³, +10.8%) and improved wettability of Cleanflow Plus fill. The findings support phased implementation and further optimization across remaining cells.
Comparative Deep Learning Models for Indonesian Gold Price Forecasting Albi Pernata Jomantara Putra; Baginda Mi’raj Williamsyah; Achmad Rizal; Favian Dewanta; Anggunmeka Luhur Prasasti; Said Ziani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2608

Abstract

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. 
Corrosion Inhibition of Mild Steel by Ethanolic Terminalia microcarpa Decne Leaf Extracts in 3.5% NaCl Solution: A Gravimetric and Surface Analysis Studies Darwin Reyes; Bon Mar Corpuz
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2745

Abstract

Mild steel is widely used but vulnerable to corrosion, with conventional inhibitors posing toxicity risks. This study evaluated the leaf extracts of Terminalia microcarpa Decne, also locally known as kalumpit, as a green corrosion inhibitor for mild steel in a 3.5% NaCl solution. The prepared extracts, characterized by FTIR, were found to contain phytochemicals with inhibition potential. Weight loss measurements showed that inhibition efficiency was concentration-dependent, but was not influenced by immersion time. SEM and UV-vis spectroscopy confirmed the formation of a protective surface barrier on the metal. The adsorption process followed a Temkin isotherm model, consistent with physical adsorption. These findings indicate that Terminalia microcarpa Decne leaf extracts are a promising and safe alternative for corrosion inhibition.
Digital Lean-Process Framework for Creative-Industry Efficiency (Case: Sipirok, Indonesia) Ahmad Rizki Harahap; Ernita; Tri Martial; Sri Ariani Safitri; Tetty Tiurma Sipahutar
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2785

Abstract

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.
Comparative Evaluation of Automatic Labeling and Modeling Strategies for Indonesian Sentiment Analysis: Methodology and Performance Evaluation Khoiriya Latifa; Agung Handayanto; Nur Latifah Dwi M.S; Rahul Bhandari; Ton Nguyen Trong Hien; Doston Pirnazarov
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2862

Abstract

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.
A CIA-based Sustainable Security Risk Mitigation Model for E-Certificate Systems Teguh Nurhadi Suharsono; John Choi; Raden Ricky Agusiady; Didin Saepudin; Sukadwilinda; Heri Purwanto; Peti Savitri; Ketut Abimanyu Munastha
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2912

Abstract

E-certificates are increasingly adopted across sectors, yet existing studies have not developed an integrated risk mitigation model that combines CIA-based sustainable security with operational and stakeholder perspectives. Current frameworks primarily address isolated technical risks or focus on general PKI security, leaving a gap in holistic modeling tailored to end-to-end e-certificate implementation. This study addresses this gap by proposing a Sustainable Security Risk Mitigation Model for e-certificate systems, guided by the CIA triad—Confidentiality, Integrity, and Availability. A mixed-methods approach was employed, including literature analysis, a Focus Group Discussion (FGD) with industry, government, and academic stakeholders, and expert evaluation using CIA-based scoring on a Likert scale. The empirical data include qualitative perspectives gathered from the FGD and quantitative assessments from expert validation. The proposed model operates in a continuous cycle consisting of risk assessment, mitigation planning, deployment and monitoring, and iterative improvement, ensuring that security controls adapt to emerging threats. Results show that the model achieves an average security validation score (asv) of 4.67, outperforming other existing risk mitigation models in CIA-based evaluation. The findings indicate that institutions can use the model as a practical framework to strengthen e-certificate governance, improve resilience against cyber threats, and support sustainable information security management.
Camera-Based Smart Mirror with Machine Learning for Postural Analysis: System Development and Reliability Evaluation Fitri Yani; Yoga Sahria; Siti Nadhir Ollin Norlinta; Riska Risty Wardhani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3097

Abstract

Early postural assessment using camera-based systems remains technically challenging due to variability in user positioning and limited evaluation of measurement repeatability. This study presents the development and repeatability evaluation of a smart mirror system for automated postural analysis using pretrained pose estimation and rule-based geometric classification. The system consists of a fixed camera mounted above a mirror and a connected computing device for real-time processing and visual feedback. Anatomical landmarks were detected from standardized anterior, posterior, and lateral views using an AI-based pose estimation model, and postural asymmetry was quantified using bilateral distance ratios and angular deviation thresholds derived from literature. Reliability was evaluated through repeated measurements to assess the consistency of landmark detection and postural classification outputs. Forty adolescents (age 12.8 ± 0.56 years; 28 males, 12 females) participated in present study. The system intra-rater reliability was evaluated by calculating Intraclass Correlation Coefficients (ICC) for the landmark data and Cohen's Kappa for posture classifications. The system demonstrated excellent reliability for key landmarks in scapula (ICC = 0.98, 95%CI 0.97-0.99) and hip-knee-ankle (ICC = 0.98, 95%CI 0.98-0.99). The classifications for scoliosis assessment also showed excellent agreement (κ = 0.90). These results indicate that the proposed system can produce repeatable posture measurements under controlled conditions; however, this study evaluates repeatability only and does not assess diagnostic accuracy or clinical validity. Further validation against clinical reference standards is required before broader application. 
Integrated Sustainable Manufacturing and Waste Management Framework for Medium-Density Fiberboard (MDF): Finite Element Methods-Based Structural Optimization for Bookshelf Applications Sri Handyani; Rizki Setiadi; Listiyono Budi; Wahyu Caesarendra
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3440

Abstract

Medium-density fiberboard (MDF) is commonly used in furniture manufacture because of its consistent qualities, low cost, and ease of processing. However, its relatively short lifespan and rising market demand have resulted in substantial waste generation, posing serious environmental and disposal concerns. This study provides an integrated sustainable manufacturing and waste management framework for MDF that incorporates artificial intelligence technology and finite element method (FEM)-based structural optimization for bookshelf applications. The structural performance is evaluated by numerical simulations focusing on von mises stress, displacement, and safety factor. These findings indicate that combining FEM-based design optimization with intelligent waste management strategies might enhance the structural performance and sustainability of MDF products. This study emphasizes the necessity of merging advanced simulation, artificial intelligence, and life-cycle assessment methodologies to create intelligent, efficient, and ecologically responsible wood-based manufacturing systems.
CFD Simulation of Flame Characteristics Resulting from Volatile Matter Combustion of Various Biomass Pellets Ramavi Akbar Akhsanul Fitrah; Ratna Dewi Kusumaningtyas; Dwi Widjanarko; Catur Rini Widyastuti; Muslikhin Hidayat; Muhammad Aziz
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3591

Abstract

While biomass is a promising carbon-neutral alternative to coal, the specific volatile matter (VM) flame characteristics of diverse biomass pellets, particularly water hyacinth, remain under-researched. This study uses a numerical CFD approach (Ansys Fluent) to investigate how varying VM fractions influence flame structure in a 2D planar slice of the furnace block (25 cm width). Simulations employed the SST k–ω turbulence and Eddy dissipation model to capture mixing-limited chemical reactions. Boundary conditions were based on experimental configurations using a 0.05 m/s air inlet velocity. Results using CO-based flame-tip markers revealed that water hyacinth (VM: 63.5 wt%) produced a peak temperature of ~1,400°C at 75 cm above the fuel, while rice husk and bagasse (VM: 59–77 wt%) exhibited longer, more intense hot plumes compared to the localized heat profile of coal. These findings demonstrate that biomass generates more dispersed combustion zones, aiding in furnace hot-spot prevention and air control optimization. A limitation of this study is that findings are based solely on numerical simulations without direct experimental validation, although the model replicates physical furnace configurations. These results provide a foundation for developing sustainable biomass–coal co-firing technologies.

Page 35 of 36 | Total Record : 352

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