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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 305 Documents
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Spatial Analysis of Waste Management Facility Distribution Using GIS Arsanti, Vidyana; Kharisma, Rizqi Sukma; Ardiansyah, Ivan; Nugroho, Bayu; Ihsan Fajruna, Muhammad; Zahra Deswanti, Luthfia; Fais Al Qori, Muhammad
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.v6i4.996

Abstract

Recently, waste has become an extraordinary phenomenon that has attracted the attention of all levels of society: authorities, local governments, environmentalists, and regional stakeholders at the village level. Based on DIY Regional Regulation No. 3 of 2013 concerning the Management of Household Waste and Waste Similar to Household Waste and Sleman Regency Regional Regulation No. 6 of 2023 concerning the Implementation of Waste Management, efforts to minimize the amount of waste are made by each waste bank collaborating with TPS3R in Sleman Regency. Based on temporary data from 178 waste banks, there are 97 active waste banks and 32 TPS3R in Sleman Regency. The objectives of this study are (1) To determine the distribution pattern of active waste banks in Sleman Regency and (2) To determine the accessibility of active waste banks to TPS3R locations. This study uses the nearest neighbour analysis method, and the accessibility of active waste bank locations to TPS3R locations is measured using the buffering method—data processing using a Geographic Information System (GIS). The results of this study indicate (1) the distribution pattern of active waste banks in Sleman Regency based on the nearest neighbour ratio value is 0.861485 (<1), indicating a spatial pattern that tends to be clustered or spread in groups; (2) the accessibility of active waste banks to the TPS3R location has not shown an even pattern, from 32 TPS3R only 10 TPS3R have two waste banks, the rest 0 - 8 waste banks. The buffering distance shows that the closer the two locations are, the more effective and efficient waste management will be, with a maximum accessibility distance of 4.1 km.
Risk Mitigation Strategies for Sustainable Poultry Supply Chain Management Haswika; Agus Mansur; Meilinda F. N. Maghfiroh
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.v6i4.997

Abstract

The livestock sector is an important pillar in providing animal protein and sustaining the rural economy. However, the sector faces major challenges from environmental and socio-economic issues, such as climate change and environmental degradation, which can threaten its sustainability. Negative impacts such as environmental contamination can reduce production quality and quantity and increase supply chain operational costs. This study aims to identify effective risk mitigation strategies to reduce these negative impacts and improve the sustainability of supply chain management. Data were collected from laying duck farms and analyzed using the House of Risk (HOR) method with a Phase 1 and 2 approach. This approach allows the identification of the most critical risks and risk agents and mapping mitigation priorities. Key findings indicate that providing drugs or vaccines to prevent animal virus outbreaks is the highest priority mitigation strategy, while strategic policy decision-making has the lowest priority. Overall, 15 risks and 21 risk agents were identified. This study implies that the implementation of effective mitigation strategies can significantly reduce operational risks, strengthen the resilience of the livestock sector, and support the sustainability of supply chain management as a whole.
Assessment of Abiotic Factors for Sea Turtle Nesting Suitability in Coastal Bays Ikegwu, Chukwudi; Nuryanto, Agus; Sastranegara, Moh. Husein
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.v6i4.998

Abstract

Cilacap Bays, critical nesting areas for sea turtles, face growing habitat disturbances from tourism. However, studies on nesting suitability in these regions remain scarce. This research assesses the abiotic factors influencing sea turtle nesting in Cilacap Regency, Indonesia, across eight observation stations. Key ecological parameters—land surface temperature (28°C - 36.3°C), pH (mean 6.8), sand particle size (0.212-0.500 mm), beach slope (11.50%-20.99%), and beach width (28.8m-81.8m)—were evaluated. The results highlight Sidaurip Beach as the most suitable for nesting due to optimal environmental conditions, with Station (SP1) being particularly favorable for producing male hatchlings due to its suitable 28°C temperature. These findings suggest targeted egg relocation to SP1 could help address gender imbalances, ensuring long-term population sustainability. This research provides valuable insights for sea turtle conservation and supports future policy efforts to protect nesting sites in Cilacap amidst growing environmental pressures
Advances in Deep Learning for Skin Cancer Diagnosis Naeemah, Maysaa R.; Kamil, Mohammed
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.v6i4.1002

Abstract

The most prevalent type of cancer worldwide is known as skin cancer. Early detection is critical because if left undiagnosed in the primary stage, it might be fatal. Although there are differences within the class and high inter-class similarities, it is too difficult to distinguish with the naked eye. Owing to the disease's global prevalence, a number of deep learning based automated systems were created thus far to help doctors identify skin lesions early on. Using pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks (CNNs), we trained VGG19 on the HAM10000 dataset. The optimal performance was observed with FT. The model that was created, which yielded an accuracy that was greater overall than the one used in transfer learning, was 82.4±1.9 %. By offering a second opinion and supporting the clinician's diagnosis, this performance could lower morbidity and treatment costs.
Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction Henri Tantyoko; Nurjanah, Dade; Rusmawati, Yanti
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.v6i4.1003

Abstract

Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.
Financial Performance Assessment of Flat Buildings Using Life Cycle Cost and Cost–Benefit Analysis Velantika, Griselda Junianda; Mikhail, Reguel; Putri, Karina Meilawati Eka; Widowati, Elok Dewi; Alghiffary, Rizqi; Akbari, Muhamad Fauzan
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.v7i1.1005

Abstract

Buildings resulting from construction projects are durable assets and decisions related to construction projects have enduring impacts. In many cases, building owners prioritize only the initial costs, such as building design, construction, and equipment costs, while neglecting the future operation and maintenance costs. This research studies life cycle costing (LCC) analysis to evaluate the financial feasibility of urban housing. The LCC calculates all the costs incurred and benefits during the building's operation. The cost is generated from construction, operational, and maintenance costs. At the same time, the benefit breaks down into flat rental costs, retail rental costs, and parking costs. The costs incurred are estimated over 25 years, and the parameters of feasibility are net Present Value (NPV), Benefit-Cost Ratio (BCR), and Internal Rate of Return (IRR). The study generates negative NPV, BCR < 1, and 0.61% of IRR. It indicates that the project is not feasible. This research gives alternatives to make the project feasible. This study employed a trial-and-error approach to ascertain the viability of investing in flat rentals by systematically adjusting rental rates. Incremental adjustments to rental rates are tested by a series of rate hikes of 50%, 100%, 150%, and 200% using a trial-and-error approach. The project will become feasible if the flat rate increases to 150-200% of the initial rental rate.
Non-Verbal Cues in Interactive Systems: Enhancing Proactivity through Winking and Turning Gestures Binti Anas, Siti Aisyah; Mazran bin Esro; Ahamed Fayeez bin Tuani Ibrahim; Yogan Jaya Kumar; Vigneswara Rao Gannapathy; Yona Falinie binti Abd Gaus; R. Sujatha
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.v7i1.1011

Abstract

This investigation investigates the extent to which proactive behaviours in interactive objects—specifically animated eyes that exhibit behaviours such as blinking and turning—improve user interaction. Through a two-phase process, we investigate the influence of these behaviors on users’ perceptions of proactivity in both physical and virtual environments. In Phase I, we conducted a real-world study using a tangible box with animated eyes to evaluate user responses to expressive behaviours in single- and multi-person interactions. The results indicate that blinking significantly improves perceptions of the box’s intentionality and engagement, thereby fostering a more robust sense of proactivity. Phase II expands this investigation to a virtual environment, where 240 participants on Amazon Mechanical Turk (MTurk) participated, thereby validating the real-world findings. The online study confirms that perceived proactivity is consistently increased across contexts by blinking and turning. These findings indicate that integrating basic, human-like behaviors into interactive systems can enhance user engagement and provide practical advice for the development of sustainable, low-complexity interactive technologies. These discoveries facilitate the future development of resource-efficient and accessible human-computer interaction and robotic systems by simulating intentionality through minimal behavior.
The Effects of Extraction Temperature on the Physicochemical Properties of Mangrove-Derived Glucomannan (Bruguiera gymnorhiza) Wibawanti, Jeki; 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.
Current Scenario of Maintenance 4.0 and Opportunities for Sustainability-Driven Maintenance Suhas H. Sarje; A. Kumbhalkar, Manoj; N. Washimkar, Dinesh; H. Kulkarni, Rajesh; D. Jaybhaye, Maheswar; Hussein Al Doori, Wadhah
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.v7i1.1028

Abstract

Industry 4.0, a shift from Industry 3.0, aims to enhance productivity and efficiency in operations and supply chain management. Maintenance plays a crucial role in this process, and IoT-enabled (Ind. 4.0) condition monitoring is a key component of this technology. However, challenges persist in implementing effective IoT-enabled condition monitoring solutions. The triple bottom line perspective (Economical, Ecological, and Social) is also crucial for realizing Ind. 4.0. This paper investigates the state of IoT-enabled industrial condition monitoring (Maintenance 4.0) and sustainability-driven maintenance (Maintenance 5.0), focusing on the challenges associated with implementing these concepts. The IoT-enabled technologies are divided into three layers: the application layer, the networking layer, and the physical layer. The physical layer, the lowest layer, faces numerous challenges in realizing maintenance 4.0 effectively. A new system configuration for vibration-based condition monitoring in an Ind. 4.0 environment is proposed to address these shortcomings. Wi-Fi technology is found to be the best option for high-throughput communication needs in the current scenario. The literature review reveals that while the economic aspect of maintenance 5.0 has been thoroughly examined, the environmental and social aspects have not been thoroughly assessed. Future research should focus on developing a new sustainable maintenance model that incorporates IoT-enabled technologies and investigates sustainable performance indicators to understand sustainability aspects quantitatively.
Enhancing Security in Wireless Mesh Networks: A Deep Learning Approach to Black Hole Attack Detection Mansi Bhonsle; Gunji Sreenivasulu; Kilaru Chaitanya; Dhumpati Raghu; Gunti Surendra; Konduru Kranthi Kumar; Mandalapu Srinivasa Rao; Kandukuri Prabhakar; Vamsi Krishna Vuppu
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.v7i1.1036

Abstract

Wireless Mesh Networks (WMNs) are susceptible to various security threats, including black hole attacks, where malicious nodes attract and drop packets, disrupting network communication. Traditional security mechanisms are often inadequate in detecting and mitigating these attacks due to their dynamic and evolving nature. In this paper, we propose a novel deep learning-based defense mechanism against black hole attacks in WMNs. It utilizes Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to analyze network traffic patterns and detect abnormal behavior indicative of black hole attacks. The proposed approach offers several advantages, including the ability to adapt to new attack patterns and achieve high detection accuracy. The evaluations of this method using an NSL KDD   demonstrate its effectiveness in mitigating black hole attacks. Results indicate a significant improvement in attack detection rates compared to traditional rule-based systems, reducing both false positives and the overall impact of such attacks on network performance. The proposed solution not only strengthens WMN security but also has the potential to adapt to evolving attack strategies through continuous learning. This research paves the way for future advancements in adversarial learning and autonomous, self-healing security systems for mesh networks. It offers scalable solutions to secure critical infrastructure like smart cities and IoT ecosystems, ensuring reliable communication. Integrating Deep Learning Algorithms security in WMNs enhances resilience against evolving cyber threats in next-generation wireless networks.

Page 14 of 31 | Total Record : 305

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