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All Journal Knowledge Engineering and Data Science Aptisi Transactions on Technopreneurship (ATT) Jurnal Bahan Alam Terbarukan
Puzi, Asmarani Ahmad
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Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

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Surface Treatment of Wood Polymer Composites from Straw Fiber Using Argon Plasma Jet Injection: Enhancing Adhesion Properties Harianingsih, Harianingsih; Astuti, Widi; Widyastuti, Catur Rini; Wulandari, Retno; Puzi, Asmarani Ahmad
Jurnal Bahan Alam Terbarukan Vol. 13 No. 2 (2024): December 2024 [Nationally Accredited Sinta 2]
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jbat.v13i2.5572

Abstract

The increasing demand for environmentally friendly materials has driven research into biodegradable composites. Straw fiber, an abundant agricultural byproduct, remains underutilized as a filler material in Wood Polymer Composites (WPC). To enhance its application potential, this study investigates the surface treatment of straw fiber/polyvinyl alcohol (PVA) matrix composites using a plasma jet with argon injection. The use of cold plasma as a treatment method is expected to improve the interfacial bonding between the straw fiber and the PVA matrix, thereby enhancing composite properties. The research employs several analytical methods, including morphological analysis of straw fibers before and after plasma jet treatment using Scanning Electron Microscopy (SEM), contact angle measurements, and emission intensity analysis via Optical Emission Spectroscopy (OES). The results reveal significant morphological changes in the straw fiber surface after plasma jet treatment, characterized by increased roughness and improved adhesion with the PVA matrix. The emission intensity analysis identifies peak reactive argon species at λ = 698.223 nm to λ = 778.398 nm, N₂⁺ at λ = 386.685 nm to λ = 445.289 nm, N₂ at λ = 324.768 nm to λ = 377.983 nm, and atomic oxygen spectral triplets at λ = 780.341 nm to λ = 830.867 nm. Moreover, the decrease in the contact angle from 90° to 57° over 60 minutes demonstrates a transition in the composite's surface properties from hydrophobic to hydrophilic. These findings highlight the effectiveness of plasma jet treatment in modifying straw fiber surfaces for improved composite performance, paving the way for broader applications of biodegradable WPC materials.
Vision-Based Vehicle Classification for Smart City Ismail, Ahsiah; Ismail, Amelia Ritahani; Shaharuddin, Nur Azri; Ara, Muhammad Afiq; Puzi, Asmarani Ahmad; Awang, Suryanti; Ramli, Roziana
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.446

Abstract

Vehicle detection systems are essential for improving traffic management, enhancing safety, supporting law enforcement, facilitating toll collection, and contributing to smart city initiatives through real-time monitoring and data analysis. With the rapid growth of smart city technologies, the need for efficient, scalable, and high-accuracy vehicle detection models has become increasingly critical. This study aims to propose an advanced vehicle detection system using Convolutional Neural Networks (CNNs) in combination with the YOLOv5 model, which is known for its high-speed performance and superior accuracy in image recognition tasks. The proposed model is evaluated using a custom-trained YOLOv5s model, tested on a dataset comprising 1460 images of vehicles. These images are divided into five classes which are cars, motorcycles, trucks, ambulances, and buses. Performance evaluation metrics such as precision, recall, and mean Average Precision (mAP50-95) are used to assess the model's effectiveness. The results indicate that the YOLOv5-based model achieved impressive detection accuracy, with precision, recall, and mAP values exceeding 87%. The proposed system demonstrates its robustness in detecting and classifying various vehicle types across different conditions, including small, partially visible, and distant vehicles. The findings suggest that this model holds significant potential for real-world applications in urban traffic management and smart city infrastructure.
Social Distancing Monitoring System using Deep Learning Ismail, Amelia Ritahani; Affendy, Nur Shairah Muhd; Puzi, Asmarani Ahmad
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

COVID-19 has been declared a pandemic in the world by 2020. One way to prevent COVID-19 disease, as the World Health Organization (WHO) suggests, is to keep a distance from other people. It is advised to stay at least 1 meter away from others, even if they do not appear to be sick. The reason is that people can also be the virus carrier without having any symptoms. Thus, many countries have enforced the rules of social distancing in their Standard Operating Procedure (SOP) to prevent the virus spread. Monitoring the social distance is challenging as this requires authorities to carefully observe the social distancing of every single person in a surrounding, especially in crowded places. Real-time object detection can be proposed to improve the efficiency in monitoring the social distance SOP inspection. Therefore, in this paper, object detection using a deep neural network is proposed to help the authorities monitor social distancing even in crowded places. The proposed system uses the You Only Look Once (YOLO) v4 object detection models for the detection. The proposed system is tested on the MS COCO image dataset with a total of 330,000 images. The performance of mean average precision (mAP) accuracy and frame per second (FPS) of the proposed object detection is compared with Faster Region-based Convolutional Neural Network (R-CNN) and Multibox Single Shot Detector (SSD) model. Finally, the result is analyzed among all the models.
Co-Authors Affendy, Nur Shairah Muhd Ara, Muhammad Afiq Awang, Suryanti Catur Rini Widyastuti, Catur Rini Harianingsih, Harianingsih Ismail, Ahsiah Ismail, Amelia Ritahani Ramli, Roziana Retno Wulandari Shaharuddin, Nur Azri Widi Astuti