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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 272 Documents
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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 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.
A Review of Factors Affecting the Mechanical Performance of PLA in FDM 3D Printing Saefudin, Slamet; Samsudi Raharjo; Ilham Yustar Afif; Syarifudin; Purnomo; Muhammad Omar Rusydi; Kuzmin Anton; Muhammad Subri; M. Edi Pujianto
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/chs1gc62

Abstract

3D printing has rapidly evolved due to its significant advantages in rapid prototyping. 3D-printed products for industrial applications require stable mechanical properties, which are influenced by various factors. The lack of a comprehensive discussion addressing the factors affecting mechanical properties is the main reason for this review. This article aims to provide an overview of Fused Deposition Modeling (FDM) 3D printing concerning the factors that influence the mechanical performance of FDM 3D products using polylactic acid (PLA) material. The article covers the impact of material factors, process parameters (such as layer thickness, infill patterns, print orientation, infill patterns, infill density, infill width, temperature, and printing speed), as well as post-processing treatments as key considerations. The contribution of this article is to explain to researchers and industry practitioners the factors that affect the mechanical performance of FDM 3D printed products. 
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.
Exploring Biochar Briquettes from Biomass Waste for Sustainable Energy Heriyanti, Andhina Putri; Bakri, Sitty Nur Syafa; Jabbar, Abdul; Kholil, Putri Alifa; Amelia, Rizki Nor; Savitri, Erna Noor; Rifaatunnisa; Siti Herlina Dewi; Habil Sultan
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.1311

Abstract

The increasing demand for renewable energy necessitates sustainable alternatives such as biochar briquettes derived from agricultural waste. This study aims to optimize the production process and evaluate the physical, mechanical, and combustion properties of biochar briquettes made from corn residues, rice husks, and coconut shells. The methodology includes biomass carbonization, binder ratio optimization, and systematic testing of key quality parameters such as moisture content, density, ash content, and calorific value. Results indicate that an optimal biomass-to-binder ratio yields a high calorific value (7,192 kcal/kg) and low ash content (3.57%), enhancing combustion efficiency. Maintaining moisture content below 10% enhances ignition and prolongs burning time. These findings highlight biochar briquettes' role in carbon sequestration, biomass conversion, and sustainable waste management, supporting the circular economy and reducing environmental pollution. Biochar briquettes offer a clean, accessible energy solution, contributing to global energy security and climate change mitigation.

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