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asset@upgris.ac.id
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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 50 Documents
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Search results for , issue "Vol. 6 No. 4 (2024): August-October" : 50 Documents clear
The Effects of Extraction Temperature on the Physicochemical Properties of Mangrove-Derived Glucomannan (Bruguiera gymnorhiza) Jeki Wibawanti; Zulfanita; Norhaslinda Arun; Anang Mohamad Legowo; Sri Mulyani; Sapto Pamungkas
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1026

Abstract

This study investigates the impact of different extraction temperatures on the physicochemical properties of glucomannan derived from mangrove fruits (Bruguiera gymnorhiza). Various extraction temperatures ranging from 45°C to 85°C were utilized. Significant differences (p < 0.05) were observed in solubility (58.41% ± 2.45), total reducing sugar content (0.39% ± 0.09), yield (35.13 ± 2.95), and L* color value (71.97 ± 1.53), while no significant differences (p > 0.05) were found in a* and b* color values. These findings have implications for expanding the applications of Bruguiera and advancing research on Bruguiera glucomannan. Scanning electron microscopy (SEM) analysis revealed an increase in the cross-linking density of glucomannan molecules.
Typology Model Based Building and Land Infrastructure Structure Organization and Duties Bali Area Police Ni Ketut Natalia Wulansari; I Nengah Sinarta; Agus Kurniawan
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1053

Abstract

Polsek was a police station in the district that provides police services to the community. There are four types of Polsek, namely A, B, C, and D. Polsek in the Bali Area are 58 with types B and C. Polsek is expected to have adequate supporting infrastructure such as Integrated Police Service Center rooms, facilities for the disabled, children's playgrounds, breastfeeding rooms, detention cells, and parking, open spaces. However, the land area is only sometimes adequate. The analysis used interview methods, observation, literature studies, and SWOT analysis. The results show that Polsek's need for land Type B is 631.54 m2, and Type C is 377.14 m2. The SWOT analysis was in the strength and threat quadrant, so a policy is needed to evenly distribute the development of each Polsek type B and C, starting from land acquisition by the main tasks and functions of the police in serving the community.
Posthumanist Technologies in Business: AI and Cloud Computing for Global Optimization and Ethical Challenges Jhonny Richard Rodriguez Barboza; Oscar David Carreño-Flores; Luis Miguel Davila-Zamora; Hans Manuel Jalixto-Erazo; Miguel Alfonso Oré de los Santos; Orlando John Cruces-Torres; Ricardo Edmundo Ruiz-Villavicencio; Danny Villegas-Rivas
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1064

Abstract

. This study explores the integration of artificial intelligence (AI) and cloud computing within posthumanist technologies, focusing on their impact on business optimization and information security. A systematic review of 40 studies across sectors such as banking and retail highlights the benefits of AI in automating tasks and enhancing decision-making, while cloud computing provides flexibility and scalability. However, risks like data privacy issues, algorithmic bias, and cybersecurity vulnerabilities demand attention. The research emphasizes the need for ethical frameworks and security strategies to mitigate these risks. Additionally, it stresses the importance of equitable access to these technologies for small and medium-sized enterprises (SMEs) and marginalized communities. The study provides actionable recommendations for businesses and calls for future research on the long-term societal implications of these technologies
A New Approach to Measuring Institutional and Researcher Contributions to the SDGs: Combining Data from Elsevier SciVal and VOSviewer Fungky Dyan Pertiwi; Dhimas Cahyo Anindito; Ilham Habibi; Saifudin; Suroto Munahar; Bagiyo Condro Purnomo; Yun Arifatul Fatimah; Budi Waluyo; Muji Setiyo
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1076

Abstract

The Sustainable Development Goals (SDGs), established by the United Nations in 2015, are a comprehensive global framework that addresses social, economic, and environmental challenges through sustainable development. This study examines the role of universities, specifically focusing on the Department of Mechanical Engineering at Universitas Muhammadiyah Magelang (UNIMMA), in contributing to the SDGs. The study utilized data from Elsevier’s SciVal to analyze the department’s contribution to the SDGs through scientific publications in the Scopus database. A total of 97 out of 156 articles published by nine researchers from the department were found to contribute to various SDGs, with a significant focus on Goals 7 (affordable and clean energy), 17 (partnerships for the goals), 9 (industry, innovation, and infrastructure), and 12 (responsible consumption and production). The study highlights the department’s collaborative efforts and alignment with global sustainability goals. In addition, VOSviewer was used to map the research collaboration network within the department, revealing strong contributions to energy efficiency, sustainable technologies, and climate action. However, the department's research shows limited contribution to social SDGs such as poverty alleviation and gender equality. By mapping the university’s contributions to the SDGs, this study helps faculty members identify opportunities for targeted research collaborations, address gaps in SDG contributions, and enhance partnerships with researchers from other institutions, thus broadening the university’s impact on global sustainable development goals.
Web Server Based Electrical Control System Analysis for Smart Buildings Masnur Masnur; Syahirun Alam
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1120

Abstract

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios
Enhancing Bus Body Assembly Efficiency: Comparative Analysis of Ranked Positional Weight and Region Approach at PT. ABC Prasetyo, Herry Dwi; Aidil, Joumil
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.675

Abstract

In general line balancing problems occur in assembling industries compared to manufacturing industries. Problems that often occur on a production line can usually be seen from the presence of a high work in process bottleneck. The problem faced by the company in the production process is the level of efficiency of the workforce and production machines which are still less than optimal, due to the imbalance of the workload between work stations caused by delays in materials from the warehouse, then the operator usually waits for directions from the foreman first, then waits for the material processing process from other station. Therefore, in the bus body assembly process, it is necessary to make an analysis or calculation of the balance of the bus body assembly process so that it can run smoothly. In the line balancing study using the ranked positional weight and region approach methods, the results of the ranked positional weight method for the bus body assembly line efficiency were 78.43%, then for the balance delay of the bus body assembly 21.57% and idle time 1189.16 minutes. After data processing using the region approach method for the bus body assembly line efficiency, the results were 64.84%, then for the balance delay of the bus body assembly 35.16% and idle time 2344.75 minutes
Risk Mitigation in Cold Chain Sytem using ANP and FMEA : A Case Study of PT XYZ Rohmah, Lailatul; Aryanny, Enny
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.690

Abstract

PT. XYZ is a company that focuses on exporting marine products, especially products for surimi products, PT. XYZ experienced problems in cold chain system activities such as declining quality of fish raw materials, limited cold storage capacity, diversity of supply quality and the risk of overloud storage, resulting in the inhibition of cold chain system activities. The purpose of this study is to identify the causes and provide appropriate mitigation strategies so that risks can be minimized by the Company. This study uses an integration method between ANP and FMEA, so that a WRPN (Weighted Risk Priority Number) value can be produced. Based on the results of the analysis using the integration of the two methods, WRPN was obtained with the highest priority on quality risk factors of 170.713 and storage risk factors of 153.087 so that both risk factors are classified as high risk and need to be mitigated. The mitigation measures provided include separation of contaminated fish, monitoring, microbiological testing, compliance with standards, SOP training, cold storage maintenance, cold storage forecasting, using the FIFO method, supervision of the use of cold storage.
Synthesis and Characterization of Nanoparticle Calcium Oxide (CaO) from Blood Calm Shell by Precipitation Methods Amri, Aisy Aulia; Shorea, Zahranisa; Astuti, Caecilia Puji
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.765

Abstract

Blood Clam (Anadara granosa) contains high calcium carbonate which can be utilized in various fields by being used as nanotechnology. The shell contains 98.7% CaCO3 making it a sustainable material source. The research aims to synthesize and characterize of calcium oxide nanoparticles by precipitation methods. This method begins with crushing the shell into 100 mesh as sample. Each sample is mixed with HCl solute. After mixing, each filtrate is precipitated with KOH solute to multiple pH (7 ; 9 ; 11). The method continues with neutralizing the precipitate with water until it reach pH 7 and drying it with oven in 100oC for 1 hour. The sample will be calcinated for 3 hours in various temperature (300oC ; 500oC ; 900oC). Samples will be analyzed with SEM-EDX and XRF Analysis. Research indicates that The degree of acidity and calcination temperature do not have a significant effect on calcium oxide content. The calcium oxide content is ranged between 82,58% - 87,47% with the sizes being ranged between 550 nm - 20mm.
A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network Ardyani, Salma Shafira Fatya; Sari, Christy Atika
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.771

Abstract

The diversity of Demak batik motifs presents challenges in classification and identification. This research aims to develop a Demak batik motif classification system using deep learning and VGG16 convolutional network. A dataset of Demak batik images is collected and processed to train the model. The VGG16 architecture is modified by fine-tuning to optimize the classification performance. Results show that the modified VGG16 model achieved a classification accuracy of 98.72% on the test dataset, demonstrating its potential application in preserving and digitizing Demak batik cultural heritage.
Classification of Corn Leaf Disease Using Convolutional Neural Network Ariska, Ratih; Sari, Christy Atika; Rachmawanto, Eko Hari
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.772

Abstract

Corn is a crop that plays a major role in food supply worldwide. Known as a cereal crop with high economic value, corn is one of the most important raw materials in the agricultural industry in many parts of the world. Leaf blight is characterized by small spots that gradually enlarge and turn brown. It is a decay of foliage caused by the fungus or species Rhizoctonia solani. Leaf spot is caused by the fungus Hel-minthoporium maydis, while stem rot is caused by Fusarium granearum. From these problems, a machine learning-based solution is given to classify corn leaf diseases using the Convolutional Neural Network (CNN) algorithm. CNN are used to classify corn leaf diseases. The selection of CNN is based on its ability to extract local attributes from image data and combine them for a more detailed and abstract representation, which is better. Classification was performed using 2145 datasets for leaf blight and 1574 datasets for leaf spot. The accuracy results obtained from this study reached 99% with the last training accuracy value of 99.06% and the last validation accuracy result of 98.50%. For future research may use more modern architectures such as classification using EfficientNet B3 architecture with transfer learning or MobileNet to improve accuracy results.

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