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Journal of Engineering, Technology, and Applied Science (JETAS)
Published by Lamintang Education & Training Centre
ISSN : 27217949     EISSN : 27218090     DOI : https://doi.org/10.36079/lamintang.jetas
Core Subject : Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics
The aim of this journal is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of Engineering, Technology and Applied Science. Subject areas cover, but not limited to Physics, Chemistry, Biology, Environmental Sciences, Geology, Engineering, Agriculture, Biotechnology, Nanotechnology, Mathematics and Statistics, Computer Science, Architecture, Industrial and all other science and engineering disciplines.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 78 Documents
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Application of Wireless Sensor Network in Monitoring Quality of Irrigation Water Kien, Hoang Trung; Minh, Hoang Anh; Jeungprasopsuk, Wilasinee
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 2: August 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0602.682

Abstract

One of the things that needs to be considered to maximize the quantity and quality of agricultural products is the maintenance of irrigation water. Farmers still difficult to monitor the quality of irrigation water due to the high cost of laboratory tests and the need for real-time monitoring of water discharge into rice fields. Some of the impacts of not maintaining irrigation water in recent years include the roots rotting easily. This research aims to create a system that can detect and monitor the quality of irrigation water that will flow into rice fields according to water quality criteria in real time with wireless sensor network technology. In this research, the parameters of pH, conductivity, temperature, and dissolved oxygen are used to determine the quality of irrigation water quality. Based on the experiment results of two types of irrigation water; river water has good pH and conductivity values while the value of dissolved oxygen is poor, while borehole water has good pH, conductivity, and dissolved oxygen values. The calibration process was carried out under the procedures and succeeded well, characterized by sensors that can detect the quality value of irrigation water. In this research, the data from the sensors can be transferred to the server via the GSM module with a time difference between data of about 10-40 seconds. This indicates the performance of the sensor and detection system is running well.
Spatiotemporal Analysis and Mapping of Fire Incidents Using GIS Technology Alrwais, Omer; Alshutairi, Abdulmohsen
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 2: August 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0602.683

Abstract

Spatiotemporal fire analysis and mapping can enable policymakers to develop effective fire management strategies that optimize the allocation of necessary resources. Efficient urban fire management requires a well-coordinated regional planning and response system. The primary objective of this paper is to analyze the spatiotemporal patterns of fire incidents in Austin, Texas, utilizing geospatial technologies. This study specifically addresses deficiencies in fire protection planning in Austin, along with the new challenges posed by the city’s rapid expansion. The fire risk and vulnerability of buildings in Austin were assessed using Geographic Information System (GIS) technology. GIS was also employed to identify potential sites for new fire stations and to optimize the design of existing stations based on fire risk assessments. This paper focuses on developing a GIS-based fire information system for the Austin Fire Department to determine the optimal route to fire incidents. The research produced a fire incidence distribution map and examined fire occurrence patterns using spatial statistical methods, including location identification, frequency analysis, and evaluation of fire hazard vulnerability and exposure. Based on the findings, the establishment of new fire stations is recommended, particularly in areas with high fire risk. The fire information system developed to identify optimal routes could be integrated with construction and cadastral inventory data to form a more comprehensive decision support system.
Factors Influencing the Efficiency of Solar Energy Systems Eze, Val Hyginus Udoka; Richard, Kiiza; John Ukagwu, Kelechi; Okafor, Wisdom
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 3: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0603.748

Abstract

The efficiency of solar panels is significantly influenced by temperature and irradiance, which are crucial in solar energy conversion. As temperatures rise, solar panel efficiency typically decreases due to increased electrical resistance, resulting in lower output voltage and power production. This efficiency loss is quantified by the temperature coefficient, indicating the drop per degree Celsius above 25°C. Advanced cooling systems and optimal thermal management can mitigate these effects. Irradiance, the sunlight intensity reaching the panels, directly affects electricity generation. While higher irradiance increases efficiency by providing more photons for conversion, it can also raise temperatures, negatively impacting performance. Solar panels achieve maximum efficiency under optimal irradiance and moderate temperatures, typically 1000 W/m² at 25°C. Variations in irradiance due to geographical location, time of day, and weather conditions cause fluctuations in power output. Efficient system design must consider local irradiance patterns and utilize tracking systems to maintain optimal panel orientation. To optimize efficiency, innovative methods such as advanced materials, cooling techniques, and smart tracking systems are employed. Additionally, integrating energy storage solutions and predictive analytics helps manage environmental impacts. Proper design, installation, and maintenance strategies are crucial for maximizing solar panel efficiency and lifespan under varying conditions. Understanding the interplay between temperature and irradiance is essential for advancing solar energy technologies, and enhancing their reliability and effectiveness in diverse environments.
Blockchain Adoption in Ireland's Financial Sector and its Regulatory Challenges and Implementation Opportunities Gallagher, Fiona; O'Reilly, Aisling; Byrne, Ciarán
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 3: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0603.757

Abstract

This research explores the adoption of blockchain technology within Ireland’s financial sector, focusing on its implementation, challenges, and potential. The study adopts a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather data from a range of financial institutions and key stakeholders. A stratified sampling technique is used to select institutions based on size, including large multinational banks, mid-sized banks, and fintech startups. Purposive sampling targets key experts such as blockchain developers, regulatory bodies, and industry leaders for interviews. Surveys collect data on blockchain adoption levels, benefits, and challenges, while interviews provide deeper insights into the perspectives of stakeholders. The research findings reveal that blockchain adoption is still in its early stages but growing steadily, with larger multinational banks leading the way. Key benefits identified include enhanced transparency, security, and efficiency, while challenges include regulatory uncertainty, technological compatibility, and cybersecurity concerns. The study concludes that while blockchain holds transformative potential for Ireland’s financial sector, broader adoption is hindered by these barriers. Recommendations include increased collaboration between financial institutions, regulators, and blockchain developers, as well as clearer regulations to foster innovation while addressing security and compliance concerns.
Enhancing Industrial Sustainability in Uzbekistan through Solar Energy Adoption in Reducing Costs and Carbon Emissions Ivanov, Aleksandr Sergeyevich; Tursunov, Muhammadali Ikboljon; Akbarov, Mustafo; Rahimova, Soliha; Sharipov, Mirzo-Ulugbek; Abdullaev, Kamiljan
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 3: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0603.759

Abstract

This study evaluates the impact of solar energy adoption on industrial sustainability in Uzbekistan, focusing on both economic and environmental outcomes. A mixed-methods approach was employed, combining quantitative data from energy savings, carbon emissions reduction, and operational cost savings with qualitative analysis from interviews and field observations across three villages: Mirzo-Ulugbek, Karakol, and Fergana. The results indicate that higher adoption of solar energy, particularly in Mirzo-Ulugbek, led to significant improvements, including a 30% reduction in energy use, a 40% decrease in carbon emissions, and a 25% saving in operational costs. In contrast, Karakol, with minimal solar adoption, showed much lower reductions. The study also highlights the challenges of widespread adoption, such as high initial investment, technological limitations in integrating solar systems, and regulatory obstacles. Data were analyzed using comparative analysis, with a focus on identifying the barriers and benefits specific to Uzbekistan’s industrial context. The findings suggest that while solar energy offers substantial potential for economic and environmental sustainability, overcoming financial and infrastructural challenges is essential for its broader adoption. Future research should explore financing models, policy development, and international case studies to further facilitate solar integration into Uzbekistan’s industrial sectors
Characteristics and Applications of Bionanosilica from Betung Bamboo Leaves Esti Prihatini; Dwi Laksono, Gilang; Khairunissa, Dhiya; Rahayu, Istie; Ismail, Rohmat
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 3: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0603.766

Abstract

Nanoparticles are materials that are currently widely used in research due to their novelty and the growing number of suitable applications. Silica nanoparticles can be produced by synthesizing using several methods such as melting, coprecipitation, sol-gel, and ultrasonication. The aim of this study is to determine the most appropriate synthesis method for the production of SiO₂ nanoparticles to optimize the quality of physical properties of fast-growing wood. The synthesis of SiO2 nanoparticles used in this study utilized three different methods: acid isolation method (F1), sol-gel method (F2), and reflux method (F3). Characterization of SiO2-NPs was performed using particle size analyzer (PSA), X-ray diffraction analysis (XRD), and Fourier transform infrared spectroscopy (FTIR). The results of PSA analysis showed that the acid isolation method produced the smallest SiO2 particle size compared to the sol-gel and reflux methods. The zeta pontential value in each method shows that the particles produced are unstable because the potential zeta value produced is around -10 mV to -30 mV. The results of FTIR and XRD analysis show that the synthesized material is a SiO₂ compound with a cristobalite phase. Application of the material on jabon wood through impregnation showed an improvement in physical properties, including an increase in WPG, density, and BE, especially in the sol-gel method (F2).
Brand Logos Recognition System Using Image Processing for Food and Beverage Brands Mohamad Roslan, Aini Khadijah; Saad, Ahmad Fadli
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 3: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0603.768

Abstract

This study investigates the development of a Brand Logo Recognition (BLR) system employing Convolutional Neural Networks (CNNs), specifically designed for the food and beverage industry in Ipoh. Accurate logo recognition is vital for businesses to strengthen brand identity, monitor consumer engagement, and mitigate the misuse of counterfeit logos. Existing systems often encounter challenges related to variations in logo design, image quality, and lighting conditions. To address these issues, the research adopts a hybrid methodology that integrates the Machine Learning Life Cycle and the Software Development Life Cycle (SDLC), utilizing an iterative Agile development framework. The system incorporates CNN models for feature extraction and classification, complemented by Single Shot Detector (SSD) algorithms for object detection. A curated dataset of food and beverage logos underwent preprocessing techniques, including resizing, normalization, and augmentation, to enhance the model’s generalization capabilities. Empirical results demonstrate high accuracy in detecting and classifying logos across diverse conditions, underscoring the effectiveness of the CNN-SSD architecture. The proposed system offers practical applications for marketing analytics and consumer research, empowering local businesses to refine branding strategies and improve customer engagement. Future research directions include the exploration of multi-label classification, real-time processing, and the integration of advanced methodologies, such as generative adversarial networks (GANs), for counterfeit logo detection. This study emphasizes the transformative potential of AI-driven logo recognition systems in revolutionizing marketing practices and supporting small and medium-sized enterprises (SMEs).
Relationship Between Working Posture and Musculoskeletal Disorders Based on REBA and Nordic Body Map Analysis in Peanut Farmers Budiyanto, Tri; Sandra, Angga Maulana; Yusuf, M
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 7 No 1: April 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0701.786

Abstract

This research was conducted on peanut farmers in Manggung Hamlet, Tileng Village, Girisubo District, Gunungkidul Regency, Yogyakarta. Based on the results of observations, it can be seen that when harvesting peanuts, farmers were found in a bent and squatting position. In this position, farmers experience pain in the neck, back and knees when working. This study aims to determine the relationship between farmers' working posture and the level of Musculoskeletal Disorders (MSDs) complaints. The risk level of working postures is assessed using the Rapid Entire Body Assessment REBA method. The number of respondents in this study was 50 peanut farmers. The research results showed that assessment of working posture of peanut farmers using the REBA method, which obtained a score of 13, can be concluded from the total score in the high risk category where immediate action is needed to improve work posture. The frequency based on working posture shows that farmers who have a high-risk working posture are 21 respondents with a percentage (42%), and farmers who have a very high-risk working posture are 29 respondents with a percentage (58%). There is a relationship between the working posture of peanut farmers and the musculoskeletal disorders when they are working with a p-value of 0.034 (significant relationship).
Leveraging Scaled Agile Framework to Develop Information Systems in Finance Field Alotaibi, Dinah Dhaif Allah; Alrwais, Omer
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 7 No 1: April 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0701.791

Abstract

This research investigates the application of the Scaled Agile Framework (SAFe) in Saudi financial institutions, focusing on its impact on scalability, regulatory compliance, security, and speed in system development. The study aims to explore the benefits and challenges of SAFe adoption in a highly regulated and hierarchical industry. Data was collected from six articles, employing a qualitative analysis to examine how SAFe addresses the critical needs of financial organizations in adapting to dynamic market conditions while ensuring compliance with strict regulations. The findings reveal that SAFe enhances scalability by improving team collaboration and governance processes, which is crucial for large financial institutions. Additionally, its iterative approach facilitates ongoing regulatory compliance and allows rapid adaptation to regulatory changes. The integration of security protocols into the continuous development process helps reduce vulnerabilities, while the structured nature of SAFe accelerates product delivery, improving responsiveness to market demands. Despite these advantages, challenges include limited experience with large-scale Agile frameworks, balancing compliance with agility, and resistance to organizational change. The study highlights the need for further research, particularly primary data collection from Saudi financial institutions, to better understand the local challenges and opportunities for SAFe adoption in this rapidly evolving industry.
Optimizing Injury Detection with Autoencoder-Based Classifiers and Feature Selection Chebbi, Imen; Abidi, Sarra; Ayed, Leila Ben
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 7 No 1: April 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0701.810

Abstract

Many machine learning applications, such as injury detection systems, have made extensive use of autoencoders. For instance, it was suggested to use improved representative features in a deep autoencoder-based injury detection system to increase detection accuracy. Similarly, a feature selection based on the agricultural fertility algorithm was used to enhance injury detection systems, demonstrating the potential of feature selection techniques in improving detection performance. This study investigates the combination of autoencoder-based classifiers for injury classification and training. This method is used on the most significant feature chosen using the chi-square test (for binary values) and Pearson correlation (for continuous values). For the experiment, we have used the dataset. The study included 250 athletes, 150 of whom were women and 100 of whom were men. The average age of the study participants ranged from 18 to 22 years old. The quiz's response rate is 90.30%. The results of the trial show that the Injury Detection System outperforms previous studies and other classifier techniques, achieving a high classification accuracy of 92.27%.

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