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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 272 Documents
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Adaptive Learning Systems for Data Conversion in EHRs through Machine Learning Janardhan Deepa; Jayashree Jayaraman
Advance Sustainable Science Engineering and Technology Vol. 7 No. 2 (2025): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/aqvgnq17

Abstract

Healthcare data management has advanced with Electronic Health Records (EHRs), enhancing the efficiency of medical procedures. Machine learning applied to EHRs transitions healthcare from reactive to proactive, supporting the cost-efficiency and sustainability goals of smart cities. However, digitizing medical records introduces security risks, especially from internal threats, necessitating strong detection systems. Research into machine learning techniques, such as decision trees, random forests, and support vector machines (SVMs), shows their effectiveness in detecting EHR breaches. Balancing system usability with patient privacy remains a key challenge amid widespread data sharing. This study highlights SVMs and deep learning models as promising for improving EHR data accuracy, enhancing detection efficiency, and supporting clinical decisions. Despite advancements in AI, deep learning continues to play a crucial role in refining clinical decision systems, including translating EHR data using technologies like natural language processing (NLP). The study provides a qualitative analysis of how deep learning can optimize EHR processes while addressing security and functional challenges.
A Novel Classification Framework Using Transformer-Based Encoding and Low-Rank Tensor Fusion for Enhanced Classification and Efficiency Venkatachalam Uma; Vanmeeganathan Ganesh
Advance Sustainable Science Engineering and Technology Vol. 7 No. 2 (2025): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jgz0xe27

Abstract

This paper proposes a transformer-based framework for sentiment analysis, designed to improve both accuracy and computational efficiency across diverse datasets. The model incorporates a low-rank tensor fusion mechanism to reduce computational complexity, optimizing the transformer encoder’s performance. Through an extensive evaluation on three benchmark datasets—Airlines, CrowdFlower, and Apple—our approach demonstrates superior performance in sentiment classification tasks, achieving accuracy levels of 93.2%, 91.5%, and 92.1%, respectively. The framework utilizes standard performance metrics, including precision, recall, and F1-score, showing consistent improvements of 5-10% over traditional models. Additionally, the model's efficiency is highlighted by its reduced processing time (120 ms per sample), making it suitable for real-time applications. The ablation study reveals that components such as pre-trained embeddings and attention mechanisms significantly contribute to its performance. The results underscore the model's robustness in handling varying sentiment distributions and highlight its scalability for large-scale sentiment analysis tasks. This study provides valuable insights into the practical application of transformer-based models in sentiment analysis, offering an efficient solution for processing diverse social media data in real-time.
Molecular Identification of Color Variants of Procambarus clarkii Using the COI Gene for Taxonomic Validation Bhagawati, Dian; Husein Sastranegara, Muhammad; Agus Nuryanto; Anastasia Endang Pulungsari; Elly Tuti Winarni; Atang; Hanan Hassan Alsheikh Mahmoud
Advance Sustainable Science Engineering and Technology Vol. 7 No. 2 (2025): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/hcaz9264

Abstract

This study addresses taxonomic uncertainty surrounding color variants of Procambarus clarkii in aquaculture and conservation contexts. We investigated whether commercially significant color morphs represent distinct subspecies or phenotypic variations of a single species. Using the cytochrome c oxidase subunit I (COI) gene as a molecular marker, we analyzed four color morphs (blue, white, orange, and brown) from aquaculture facilities in Banyumas Regency. DNA extraction, PCR amplification, and sequencing were performed on 20 specimens. Results showed high sequence homology (98.7-99.8%) across all variants, confirming they belong to a single species. Genetic distance analysis revealed minimal divergence (0.2-1.3%), insufficient for subspecies classification. Phylogenetic reconstruction demonstrated specimens clustered by genetic similarity rather than color or geographic origin, indicating coloration results from genetic mutations rather than environmental adaptations. This COI-based approach provides a molecular framework for taxonomic classification of P. clarkii varieties, with implications for breeding programs, variety certification, and management of this economically important yet potentially invasive species.
Assessing Seasonal Variations in Reservoir Water Quality: Implications for Eutrophication and Pollution Management Nurdin; Agatha Sih Piranti; Sri Lestari; Iing Nasihin; Nina Herlina
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): 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.v7i1.1315

Abstract

Surface water is a strategic freshwater reserve that meets the needs of households, agriculture, livestock, industry, and research. Surface water quality is affected by anthropogenic activities and seasonal variations, which can pose ecological risks. This study aimed to assess the water quality of the Darma Reservoir, the status of water quality and trophic levels, and trends in water quality changes in the rainy and dry seasons. The study was conducted for one year, from October 2023 to September 2024, covering the rainy and dry seasons. Sampling was carried out at eight stations spread across three zones of the Darma Reservoir, namely the inlet zone, utilization zone, and outlet zone. Water quality parameters were tested using PCA, the water sample measurements were compared with water quality standards (PP/22/2021), and the Regulation of the Minister of State for the Environment number 28 of 2009 was analyzed using the STORET index. The results of the study showed differences in water quality characteristics between seasons, where the concentration of Total Nitrogen (TN) showed an increase in the rainy season, while the concentration of Total Phosphate (TP) was higher in the dry season.
Comparative Analysis of Formwork Systems: Cost Efficiency and Time Management in Construction Projects Supriyono, Auliyah Shabirah; Putra, I Nyoman Dita Pahang; Bambang Trigunarsyah
Advance Sustainable Science Engineering and Technology Vol. 7 No. 2 (2025): 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.v7i2.1320

Abstract

Formwork systems are essential for achieving efficiency and sustainability in multi-story construction. This study compares the cost and time efficiency of multiplex and aluminum formwork systems for constructing beams and slabs on the 6th to 13th floors. Field observations and a literature review were employed to gather data using a mixed-methods approach. This study adopts a mixed-methods approach, combining quantitative and qualitative methods. The findings reveal that aluminum formwork requires 60 days to complete, compared to 112 days for multiplex formwork, saving 52 days. Regarding cost, aluminum formwork amounts to IDR 1,805,910,198, offering savings of IDR 159,540,423 compared to multiplex formwork at IDR 1,965,450,621. Graphical analysis highlights the advantages of aluminum formwork in optimizing project workflows and reducing delays. These results demonstrate aluminum formwork potential to enhance efficiency, minimize material waste, and support sustainable construction practices. Future research is encouraged to explore alternative materials and labor strategies to further advance sustainability in the construction industry.
Assessment of Occupational Noise Exposure in Industrial Environments: A Case Study in Metal Casting Dwiandra, Annisa Restia; Qurtubi; Haswika, Haswika; Sugarindra, Muchamad
Advance Sustainable Science Engineering and Technology Vol. 7 No. 2 (2025): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/3aqgpy70

Abstract

Good work environment conditions can affect the increase in worker productivity. One of the work environment factors that needs to be considered is distraction. Noise problems not only interfere with concentration but can also damage workers' hearing and reduce overall work safety. Therefore, this study aims to analyze the noise level in the work area in the production process of a company engaged in the metal casting industry and provide recommendations that can reduce the negative impact on the workforce. This measurement uses Decibel X with a time interval of five seconds, and then data processing is carried out to determine the LTm5 value. The measurement results show that the level of disturbance in some work areas exceeds the recommended threshold, increasing the risk of worker health problems. To reduce this negative impact, the study provides the provision of personal protective equipment (PPE), such as earplugs, as well as the implementation of occupational safety and health (OHS) socialization. This research makes an essential contribution to the management of nuisance risks in industrial environments, especially in improving worker safety and health through effective mitigation strategies. 
Exploration of Identification at Each Phase to Advance Productivity Towards Sustainable Practices Putri, Azhahra Divia; Putra, I Nyoman Dita Pahang; Adibah Nurul Yunisya
Advance Sustainable Science Engineering and Technology Vol. 7 No. 2 (2025): 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.v7i2.1333

Abstract

This study analyzes the productivity of tower cranes in high-rise building construction projects in Indonesia, focusing on sustainable practices and advanced techniques. The rapid growth of infrastructure has heightened competition in the construction sector, making efficiency essential. Tower cranes are key to accelerating project timelines and reducing costs, but their productivity can differ between theoretical specifications and actual field conditions. Using a quantitative descriptive method, this study compares theoretical productivity based on equipment specifications with direct field observations of the Potain MCT 205 tower crane at a high-rise site. Data on cycle time and lifting volume for work such as column reinforcement and concrete pouring were collected. Results show that theoretical productivity is higher than field observations, with a 40% increase for works and up to a 92% increase per phase. The findings stress the importance of incorporating advanced planning and sustainable practices to optimize productivity, minimize delays, and reduce costs in construction projects
Optimizing Inventory Control Using Min-Max Method for Sustainable Manufacturing Process Hermawan, Prayoga Prima; Qurtubi; Haswika; Sugarindra, Muchamad
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): 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.v7i1.1337

Abstract

Inventory plays an important role in a company's production process, especially in the sustainable manufacturing industry. The inventory of raw materials such as rayon, polyester, and cotton is an essential element that needs to be controlled to maintain a smooth production process. This research aims to plan and control raw materials through the min-max method, with a focus on evaluating inventory control to identify and overcome existing problems in the raw material warehouse at a yarn and textile manufacturing company. The results show that each type of raw material has a different reorder level, which guides the company in avoiding the risk of shortage or excess stock. By applying the right reorder level, the company can improve its production efficiency and inventory management. This research contributes to the practice of inventory control in the sustainable manufacturing industry, which supports operational stability and minimizes resource wastage. The implications of the findings could expand the application of min-max method-based inventory control in other industries to support operational sustainability.
Hybrid Approaches for Advanced Medical Text Summarization: Combining TF-IDF, BERT, and Seq2Seq Models Matimpati Chitra Rupa; Ramani, Kasarapu
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): 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.v7i3.1427

Abstract

Clinicians, researchers, and healthcare professionals are confronted with the challenge of efficiently extracting relevant knowledge from vast amounts of textual data. Medical text summarization emerges as a crucial tool to address this challenge by condensing lengthy medical documents into concise, informative summaries. A comprehensive hybrid approach is proposed to address the challenges in medical text summarization by combining both extractive and abstractive methods, by integrating Term Frequency-Inverse Document Frequency (TF-IDF) of Natural Language Processing (NLP) and AutoModelForSeq2SeqLM of Large Language Model. The performance this proposed approach is compared with existing methods such as Bidirectional Encoder Representations from Transformers (BERT), Text Rank, K-means, face book BART-Large-CNN, GPT2 using ROUGE-1, ROUGE-2 and ROUGE-L metrics. The experimental results show that hybrid approach is outperforming other existing methods. Medical text summarization helps extract important information from large medical documents. This work combines two methods, TF-IDF and AutoModelForSeq2SeqLM, to create better summaries, performing better than existing techniques like BERT and GPT-2 based on ROUGE scores.
Improving Road and Sidewalk Accessibility for Persons with Disabilities: Infrastructure Challenges and Legal Compliance in Indonesia Muhammadiah, Muhammad Jabir; Ahmad Selao
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): 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.v7i1.1465

Abstract

Research to address significant challenges related to public infrastructure accessibility, especially for disabilities, regarding regulations that govern accessibility, the implementation in the field is still far from adequate. The research aims to evaluate the condition of public infrastructure, identify accessibility barriers for disabilities, and provide recommendations for future improvements. A mixed-methods approach, with participatory research methodology, provides significant contributions to disability and urban planning. Probability sampling method, with 150 respondents, physical, intellectual, and sensory disabilities, as well as experiences and challenges of accessibility. Data analysis, qualitative and quantitative methods, thematic analysis to analyze qualitative data about PWD experiences, descriptive and inferential statistical analysis for quantitative data. The findings indicate that road and sidewalk infrastructure is inadequate, with uneven surfaces, a lack of supporting facilities such as ramps, and unclear signage. Persons with disabilities are isolated from participating in public spaces, highlighting the gap between regulations and their implementation on the ground. The findings emphasize the integration of universal design in future infrastructure planning. Involving disabilities in planning results in more inclusive and effective solutions. Improving training and awareness for urban planners, along with regular monitoring of public infrastructure, ensures compliance with accessibility standards, moving towards a Smart Disability City (SDC)

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