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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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Kota semarang,
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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
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.
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.
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°.
Development of Counseling Sites with Digital Accessibility Features for the Blind and Visually Impaired Students Dini Rakhmawati; Venty; Febrian Murti Dewanto
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 Tri Lathif Mardi Suryanto; Aji Prasetya Wibawa; Hariyono Hariyono; Andrew Nafalski
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 Nurtriana Hidayati; Titin Winarti; Alauddin Maulana Hirzan
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.

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