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Mega Novita
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novita@upgris.ac.id
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asset@upgris.ac.id
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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,
Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology
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 science, engineering, and technology
Arjuna Subject : Energi (semua kategori) - Teknik Energi dan Teknologi Daya
Articles 15 Documents
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Search results for , issue "Vol 5, No 3 (2023): August-October" : 15 Documents clear
Analysis of Listen Before Talk Protocol Implementation in Terrestrial Network Using Multi-Node LoRa Communication Shafiatullaily Mahmud
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.16520

Abstract

Wireless communication today plays a very important role in technological development but often faces challenges in terms of reliability and quality of data transmission; this study implements the Listen Before Talk (LBT) protocol in the terrestrial network environment through Multi-Node LoRa communication to test its effectiveness in avoiding packet loss ending the quality of communication in variations of the land environment. Test results showed that LBT effectively produced zero percentage packet loss at 50 to 500 meters. At the same time, LoRa devices could maintain communication quality even though the Received Signal Strength Indicator (RSSI) value decreased with the distance. The findings provide positive indications of the potential of LBT protocols inning data transmission integrity in terrestrial environments and foster the development of reliable and high-quality wireless communications in complex ground network environments.
Review and Bibliometric Analysis of Biogas Power Plants in Indonesia Dhasa Ikrar Setyanansyach; Muji Setiyo; Thirunavukkarasu Raja
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.16806

Abstract

The demand for energy is increasing due to population growth, technological advancements, and a growing need for sustainable energy sources. Indonesia, which faces an energy deficit, is exploring alternatives to fossil fuels. Biogas, produced through the anaerobic fermentation of organic matter, offers a clean and sustainable energy option while addressing waste disposal issues. Therefore, this literature review examines various aspects of a biogas power plant, including a feasibility study that encompasses technical and economic analyses, generator design, trials, implementation, and post-implementation evaluation. In this review, we gathered scientific papers from Google Scholar using the keywords "pembangkit listrik biogas" between 2019 and 2022, with a focus on recent content. Patents and citations were excluded from the Google Scholar searches to ensure article relevance. Out of a total of 40 articles, 30 were rejected because they did not originate from scientific journals. The collected articles are categorized based on the materials used for biogas generation in power plants. This systematic approach yielded 10 relevant articles. Consequently, the literature reveals that various raw materials, such as palm oil mill waste (POME), livestock manure, and organic waste, hold the potential for biogas production. The results emphasize the economic feasibility of specific biogas projects, the environmental challenges they pose, and the positive impact they have on community well-being
Effect of Chitosan Variation in Starch and Cellulose Based Biofoam Ayu Lintang Cahyani; Vidrika Linda; Dody Guntama; Mubarokah N Dewi; Lukmanul Hakim
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.17126

Abstract

Styrofoam’s using as packaging is increasing. Styrofoam is difficult to decompose so alternatives such as biofoam are needed. This study explores the creation of eco-friendly packaging material by varying cellulose (0%, 3%, 5%, and 7%) and chitosan concentrations (0%, 2%, 4%, 6%, 8%, and 10%) in biofoam, aiming to replace non-biodegradable Styrofoam. Production is carried out by delignifying sugarcane bagasse and corn cobs with 10% NaOH, making tofu pulp starch and biofoam. The research focuses on tensile strength, absorption capacity, biodegradation, and morphology of the biofoam. Results indicate that chitosan concentration affects water absorption and biodegradation, while cellulose impacts tensile strength. The findings highlight the potential of biofoam made from tofu dregs, corn cobs, and sugar cane bagasse, offering a promising alternative to Styrofoam
Mangrove Tree Species Classification Based on Leaf, Stem, and Seed Characteristics Using Convolutional Neural Networks with K-Folds Cross Validation Optimalization
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.17188

Abstract

Mangrove classification plays a pivotal role in environmental monitoring and conservation efforts. In this study, our meticulously curated dataset comprised diverse mangrove tree images standardized to 250 x 250 pixels, capturing the nuances of various species. Employing advanced deep learning techniques, our models demonstrated exceptional accuracy, reaching 99.23% without K-Folds and a slightly enhanced 99.78% with K-Folds. These models exhibited outstanding consistency, showcasing recall, precision, and F1-Score metrics all surpassing 99%. Through rigorous testing in 10 experiments, both K-Folds and non-K-Folds methods consistently achieved 100% accuracy, evidenced by the presence of True Positives in every classification scenario. This remarkable performance underscores the robustness of our algorithms in precisely classifying mangrove species, offering a valuable tool for ecological research and conservation initiatives.
A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.16854

Abstract

Classification of car vehicle types has been carried out using CNN. There are weaknesses in the CNN algorithm so that it can be continued in the research we propose. This study aims to improve the previous accuracy by using the Alexnet architecture. To improve the results of the data set used we use threshold and brightness adjustment and data augmentation techniques for Reflection, Rotation, and Translation. Sample images with a resolution of 227x227x3 totaling 840 images used to represent 8 class types of cars, including Avanza, Fortuner, Freed, Inova, Pajero, Terios, Xenia, and Xpander. Alexnet with 10 epochs consisting of a total of 760 iterations, and validation is carried out every 30 iterations, the test results show that the use of the "sgdm" optimization function achieves a training accuracy of 99.74%, while the use of the "adam" optimization function produces an accuracy of 96.85%. This experiment shows the model's ability to classify the types of trainers after a success rate of 100%.

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