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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 50 Documents
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Search results for , issue "Vol. 7 No. 1 (2025): November-January" : 50 Documents clear
Development of a Robotic System for Agricultural Pest Detection: A Case Study on Chili Plants Salahuddin , Nur Sultan; Fathi Muthia Tarie; Saptariani, Trini
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 R.Kadhim, Rania; 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°.
Development of Counseling Sites with Digital Accessibility Features for the Blind and Visually Impaired Students Rakhmawati, Dini; Venty; Murti Dewanto, Febrian
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.v7i1.1157

Abstract

Current counseling services, such as those available through sikons.upgris.ac.id, lack full accessibility for students with disabilities, particularly those who are blind and visually impaired. Studies reveal significant accessibility barriers across educational websites, impeding equal access for users with disabilities. This study addresses these gaps by developing an accessible counseling platform aligned with Web Content Accessibility Guidelines (WCAG) to ensure inclusive access for all students. Using the ADDIE model's structured stages of Analysis, Design, Development, Implementation, and Evaluation, this study aims to create a technically advanced, user-centered application that enhances usability and independence for students with disabilities. Results from user acceptance testing with 11 participants indicated a high satisfaction rate of 89,33%, demonstrating that the platform effectively meets users' needs, significantly improving accessibility and usability in educational counseling services. This outcome underscores the importance of integrating accessibility standards to foster inclusivity and equitable participation in digital educational resources.
Comparative Performance of Transformer Models for Cultural Heritage in NLP Tasks Suryanto, Tri Lathif Mardi; Wibawa, Aji Prasetya; Hariyono, Hariyono; Nafalski, Andrew
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.v7i1.1211

Abstract

AI and Machine Learning are crucial in advancing technology, especially for processing large, complex datasets. The transformer model, a primary approach in natural language processing (NLP), enables applications like translation, text summarization, and question-answer (QA) systems. This study compares two popular transformer models, FlanT5 and mT5, which are widely used yet often struggle to capture the specific context of the reference text. Using a unique Goddess Durga QA dataset with specialized cultural knowledge about Indonesia, this research tests how effectively each model can handle culturally specific QA tasks. The study involved data preparation, initial model training, ROUGE metric evaluation (ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum), and result analysis. Findings show that FlanT5 outperforms mT5 on multiple metrics, making it better at preserving cultural context. These results are impactful for NLP applications that rely on cultural insight, such as cultural preservation QA systems and context-based educational platforms.
Hybrid Filtering for Student Major Recommendation: A Comparative Study Hidayati, Nurtriana; Winarti, Titin; Hirzan, Alauddin Maulana
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.v7i1.1250

Abstract

Choosing the right university major is an important decision for students, as delays or incorrect choices can harm their future careers and cause problems for academic departments. High dropout rates, which are frequently the result of poorly informed decisions, can be a considerable burden on faculty. This project aims to address these challenges by creating a recommendation system that provides individualized counsel to students based on their psychological profiles. A quantitative method was used, with questionnaires distributed to a large number of students. To verify the data's authenticity, replies were sought from students who were pleased with their selected majors rather than those who regretted their choices. The collected data formed the basis for a hybrid recommendation system that integrated Content-based Filtering and Collaborative Filtering methods. The system was then compared against standalone implementations of each filtering method to determine its usefulness in increasing suggestion accuracy. The results showed that the Hybrid Filtering strategy obtained a recommendation accuracy of 84.29%, outperforming Content-based  Filtering at 81.43% and Collaborative Filtering at 78.57%. The proposed model is easy to implement in a school or a university, as long as the required data is available. Thus, the model can help a school or university to reduce dropout rates and boost academic outcomes.
Stability Analysis of Optimized PMU Placement using Hybrid and Individual TLBO-PSO Techniques Santosh Kumari Meena; Akhil Ranjan Garg
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.v7i1.1261

Abstract

In power system to optimized PMUs is a critical task to ensure maximum network observability while minimizing installation costs. This study presents a comparative analysis of three optimization techniques: Teaching-Learning-Based Optimization (TLBO), Particle Swarm Optimization (PSO), and a hybrid TLBO-PSO approach, focusing on their efficiency in determining the best PMU placements. Individual methods, such as TLBO and PSO, are often limited by longer computation times and the requirement for a higher number of PMUs to achieve full observability. In contrast, the hybrid TLBO-PSO method demonstrates significant improvements, consistently delivering solutions with fewer PMUs, faster computation times, and higher placement accuracy. By evaluating performance of these techniques on IEEE 14bus, 30bus and 57 bus systems through simulations conducted over 100 iterations for each method in every test case. The results highlight the hybrid approach's superior efficiency compared to individual methods. Furthermore, comparisons with prior research confirm that the hybrid TLBO-PSO approach is a robust and reliable solution for minimizing PMU installations while ensuring complete system observability.
Real-World Emission Assessment of Diesel Passenger Cars in Urban Traffic: A Comparative Analysis of Compliance with Bharat Stage VI Standards Meena, Sanu; Singh, Suresh Kumar
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.v7i1.1294

Abstract

Urban air pollution, significantly influenced by vehicle emissions, poses severe health risks, particularly in rapidly urbanizing cities. This study investigates real-world emissions from diesel-powered passenger cars under mixed traffic conditions, focusing on compliance with Bharat Stage VI (BS VI) standards. Using Portable Emission Measurement Systems (PEMS), emission factors for Carbon Monoxide (CO), Oxides of Nitrogen (NOx), and the combined mass of Hydrocarbons and Oxides of Nitrogen (THC + NOx) were measured. Results revealed exceedances of 75%, 103.75%, and 40.59% for CO, NOx, and THC + NOx, respectively, underscoring inefficiencies in emission control technologies. Variability in emissions was linked to vehicle age, maintenance, driving behaviors, and challenging road conditions. These findings highlight the critical gap between laboratory-tested and real-world emissions, emphasizing the need for stricter regulations, advanced emission technologies, and public awareness campaigns. The study offers actionable insights for urban air quality improvement and policy development to reduce vehicular pollution.
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.
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.
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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