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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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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 250 Documents
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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
Sustainable Strengthening of Concrete Using Lathe Waste Steel Fibers: Experimental and FEA Analysis: Sustainable Fiber Reinforced Concrete Fawad Ahmad; Aiman Aiman Al-Odaini; Mohammad Nizamuddin Inamdar; Muhammad Majid Naeem; Muhammad Omair; Hamza Rafiq; Muaz Irfan
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.v7i1.1068

Abstract

The construction industry is growing fast and increasing the demand for concrete which requires sustainable materials. Concrete is weak in tension and needs fiber reinforcement to improve strength. This study explores lathe waste steel fibers as a sustainable option to enhance tensile properties. Local industries produce large amounts of lathe waste that can be used in fiber-reinforced concrete. Traditional destructive testing is expensive and slow because it needs heavy machinery like Universal Testing Machines. This research combines destructive and computational analysis using ANSYS 15 for finite element modeling. Cylindrical and beam specimens were tested with fiber ratios from 0 to 3 percent. Results show that 2.5 percent fiber content gives the best compressive and flexural strength. Scanning electron microscopy confirms stronger bonding as fibers break within the matrix instead of pulling out. This study proves lathe waste steel fibers improve both performance and sustainability in construction
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.
Advancements and Challenges in Additive Manufacturing: Future Directions and Implications for Sustainable Engineering Raffi Mohammed; Abdul Saddique Shaik; Subhani Mohammed; Kiran Kumar Bunga; Chiranjeevi Aggala; Bairysetti Prasad Babu; Irfan Anjum Badruddin
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.v7i1.1079

Abstract

This study explores the recent advancements in additive manufacturing (AM) and its significant effects on various industries such as aerospace, automotive, medical, and casting. The research investigates how AM has the potential to enhance design flexibility, reduce weight, and optimize material performance through developments like adaptive algorithms, topology-based process planning, and multi-objective optimization techniques. These advancements have resulted in near-net-shape casting, improved surface finishes, and enhanced structural integrity. However, the widespread adoption of AM in the commercial sector faces challenges such as high costs, limited material compatibility, and inconsistent build quality. This paper assesses these limitations and suggests solutions such as enhanced design algorithms, AI-driven process monitoring, and the creation of sustainable materials to address them. By overcoming these barriers, AM can smoothly integrate into industrial environments and revolutionize manufacturing processes. The study emphasizes the importance of further exploration of AM's potential to drive innovation, sustainability, and productivity across different sectors.
Review of Reliability of Solar Hybrid Generator System as Temporary Power Supply for Offshore Industry for Sustainable Platform Application of Environmentally Friendly Energy Sources Haryani Alamsyah; Ardiansyah; Sunardi
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.v7i1.1116

Abstract

This paper discusses the application of two combined power generation systems namely generator and Solar Panel in improving the efficiency of offshore power supply during downtime. Ensuring reliable and sustainable power in remote and limited range environments is critical for sustainable platform maintenance and sustainability. Traditional power sources, such as diesel generators, although reliable, have high carbon emissions and operational costs. Solar energy, although environmentally friendly, faces spatial constraints in offshore. A hybrid system combines photovoltaic (PV) panels and conventional generators, which provides an optimal balance between renewable energy and reliability. This study focuses on the system design, operational benefits, and its impact on wellhead platform sustainability, highlighting its efficiency and environmental sustainability.
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
Optimizing Image Preprocessing for AI-Driven Cervical Cancer Diagnosis Chandra Prasetyo Utomo; Neng Suhaeni; Nashuha Insani; Elan Suherlan; Nunung Ainur Rahmah; Ahmad Rusdan Utomo; Indra Kusuma; Muhamad Fathurachman; Dewa Nyoman Murti Adyaksa
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.v7i1.1128

Abstract

Cervical cancer ranks among the top causes of cancer-related deaths in women globally. Early detection is vital for improving patient survival rates. The multiclass classification of cervical cell images presents challenges primarily due to the notable variations in cell sizes across different classes. Conventional AI methods for diagnosing cervical cancer often rely on image-resizing techniques that overlook crucial features like relative cell dimensions, which impairs the models' ability to distinguish between classes effectively. This paper presents a novel AI-driven approach that employs constant padding to maintain the natural size differences among cells. Our method utilizes deep learning for both feature extraction and multiclass classification. We assessed the method using the publicly accessible SIPaKMeD dataset. Experimental findings indicate that our approach surpasses traditional image-resizing methods, especially in classes that are more challenging to predict. This strategy highlights AI's potential to improve cervical cancer diagnosis, offering a more precise and dependable tool for early detection. A reliable and precise AI model for diagnosing cervical cancer is crucial for promoting widespread screening and ensuring timely and effective treatment, which can ultimately lower mortality rates. By aiding early and accurate diagnosis, this approach aligns with global health efforts to alleviate the burden of cancer and other diseases, especially in areas with limited access to advanced healthcare services facilities.
Innovation of Artistic Gymnastics Equipment in Limited Space Tubagus Herlambang; Donny Anhar Fahmi; Utvi Hinda Zhannisa
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.v7i1.1147

Abstract

This study was motivated by the limited space for men's artistic gymnastics in Central Java, which generally uses a small and unrepresentative school arena, so the arrangement of equipment such as uneven bars, parallel bars, rings and saddle horses is not optimal. The aim of this study is to develop innovative multifunctional artistic gymnastics equipment in a limited space to optimise the gross motor development of junior male artistic gymnasts and to improve the effectiveness and efficiency of training. The research method uses a research and development approach with quantitative data from expert questionnaires, athletes and coaches, as well as analysis of equipment innovation based on Computer Aided Design (CAD) technology. The results of the analysis showed the maximum stress on the developed equipment, namely single bars 45.7 MPa, parallel bars 72.6 MPa, straps 29.48 MPa and saddles 92.9 MPa, which are located at the ends near the pivot point. The results of the analysis showed that the Innovation products are safe to use. The conclusion the conclusion of this research is the creation of artistic gymnastics equipment innovation in a limited space that is feasible to use.
Development of a Robotic System for Agricultural Pest Detection: A Case Study on Chili Plants Nur Sultan Salahuddin; Fathi Muthia Tarie; Trini Saptariani
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.v7i1.1152

Abstract

Chili peppers, a key agricultural commodity in Indonesia, are highly susceptible to pest infestations and diseases, leading to significant economic losses and challenges in sustainable farming. This study presents the design and implementation of a real-time pest detection system that integrates robotics, computer vision, and deep learning to enhance agricultural productivity. The system is built on a Raspberry Pi 5 and Arduino Mega Pro Mini, utilizing a camera for image capture and ultrasonic sensors for navigation. A ResNet-based model was trained on a dataset of 2,703 chili leaf images, categorized into healthy and diseased classes, achieving a detection accuracy of  91%. The system provides early warnings to farmers through a web-based interface, allowing timely intervention and reducing reliance on chemical pesticides. While promising, the system faced challenges such as environmental variability, which influenced image recognition accuracy. By automating pest detection and promoting precision farming, this innovation addresses the need for sustainable agricultural practices, contributing to global food security and reducing environmental impact.
Advancing Dermatological Image Classification: GLCM-Based Machine Learning Insights Rania R.Kadhim; Mohammed Y. Kamil
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.v7i1.1154

Abstract

The prospects to improve skin illness via the utilization of artificial intelligence algorithms is what renders this study economically important. Machine learning may assist physicians detect people quicker and more accurately. The effective identification of skin disorders using machine learning could result in the development of large and readily available digital tests. A model was used in the present study to analyze the HAM 10000 data. Two hundred images in total were chosen at random; one hundred showed dermatofibroma diseases, whereas the other hundred displayed benign keratosis. Subsequently, these images were resized to prepare for additional examination. The statistical features of the gray level co-occurrence matrix were calculated from the image dataset by changing the distances 0, 5, 10, 15 and angles 0°, 45°, 90°, 135°. Five different machine learning models were subsequently trained and assessed based on these features. The study shows that the logistic regression model accurately detects and classifies various skin diseases. The logistic regression model showed exceptional performance, exceeding the expected results in terms of accuracy 91.50%, sensitivity 93.00%, and F1-score 91.36. The results of the study were most favorable when using an angle measurement of 135°.

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