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All Journal Journal of Computer Science Advancements Journal of Moeslim Research Technik Journal of Selvicoltura Asean Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Social Entrepreneurship and Creative Technology Journal of Multidisciplinary Sustainability Asean Scientechno: Journal of Science and Technology Sharia Oikonomia Law Journal International Journal of Educational Narratives Rechtsnormen: Journal of Law International Journal of Language and Ubiquitous Learning Journal of Social Science Utilizing Technology
Sun Wei
Beijing Institute of Technology
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Other

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EMBEDDED INTELLIGENCE: EDGE COMPUTING ARCHITECTURES FOR REAL-TIME CONTROL APPLICATIONS Sayed Achmady; Nopriadi Nopriadi; Ahmad Ikhwan; Sun Wei
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.3898

Abstract

Real-time control applications demand low-latency, reliable, and energy-efficient computational frameworks. Traditional cloud-centric architectures often fail to meet these requirements due to network-induced delays, unpredictable bandwidth, and limited adaptability under dynamic workloads. The integration of embedded intelligence within edge computing environments has emerged as a promising solution to enhance responsiveness, operational reliability, and system scalability. This research investigates edge computing architectures designed for embedded intelligence, aiming to optimize latency, throughput, and energy consumption in heterogeneous hardware configurations. Experimental and simulation-based methods were employed to evaluate performance across microcontrollers, FPGAs, and CPU/GPU nodes under varying workloads and network conditions. Data collection included latency measurements, throughput analysis, task completion times, and energy profiling. Inferential analyses, including correlation and regression models, quantified the relationship between computational capacity, responsiveness, and efficiency. A robotic manipulation case study further validated the practical application of the proposed architectures. Results indicate that adaptive, edge-enabled embedded intelligence significantly reduces latency to sub-10 millisecond levels, maintains high throughput, and ensures consistent task completion under dynamic conditions. Heterogeneous architectures outperform uniform deployments in both reliability and energy-performance balance. These findings demonstrate the feasibility and effectiveness of integrating embedded intelligence at the edge for real-time control. The study provides actionable guidance for designing scalable, robust, and energy-efficient intelligent control systems.
THE ROLE OF COGNITIVE DEVELOPMENT IN ADOLESCENCE: IMPLICATIONS FOR EDUCATION AND MENTAL HEALTH Sun Wei; Chen Mei; Ryan Teo
Research Psychologie, Orientation et Conseil Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v3i3.3936

Abstract

Adolescence is a critical developmental period marked by rapid cognitive, emotional, and social changes that significantly influence learning processes and mental health outcomes. Cognitive development during this stage plays a central role in shaping adolescents’ academic engagement, self-regulation, and psychological well-being, yet it is often examined separately from educational and mental health perspectives. This study aims to investigate the role of cognitive development in adolescence and its implications for both education and mental health within an integrated framework. The research employed a quantitative cross-sectional design involving adolescents aged 13–18 years enrolled in secondary education. Data were collected using standardized instruments measuring cognitive development, educational engagement, and mental health well-being, and were analyzed using descriptive and inferential statistical techniques. The findings reveal significant positive relationships between cognitive development and educational engagement, as well as between cognitive development and mental health indicators. Adolescents with higher levels of cognitive functioning demonstrated stronger academic engagement and better emotional regulation. These results indicate that cognitive development functions as a key mechanism linking learning and mental health during adolescence. The study concludes that educational practices and mental health interventions should prioritize cognitive development as a foundational element.
CHEMICAL ENGINEERING FOR THE PRODUCTION OF HALAL-CERTIFIED GELATIN ALTERNATIVES FROM PLANT-BASED SOURCES Neneng Windayani; Michael Johnson; Sun Wei
Journal of Moeslim Research Technik Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i5.2503

Abstract

The demand for halal-certified products is increasing globally, particularly in the food industry, where gelatin—a key ingredient in numerous products—often comes from animal sources, raising concerns for halal dietary compliance. This research focuses on developing plant-based alternatives to gelatin, specifically designed to meet halal certification standards. The primary objective of this study is to explore the chemical engineering processes involved in producing gelatin alternatives from plant-based sources, ensuring they meet the physical, chemical, and sensory properties required for food applications. Using a combination of plant-based polysaccharides, such as agar, carrageenan, and pectin, this study employs extraction, hydrolysis, and gelling techniques to develop suitable alternatives. The research utilizes a systematic approach, including experimental trials, chemical analyses, and sensory evaluations, to assess the functional and sensory qualities of the plant-based gelatin alternatives. Results indicate that certain plant-derived alternatives exhibit comparable gelling, stability, and texture to traditional gelatin, while also adhering to halal certification requirements. The study concludes that plant-based gelatin alternatives offer a promising, sustainable solution for the halal food industry, with potential applications in a wide range of products such as confectionery, dairy, and pharmaceuticals. These findings provide valuable insights into the development of halal-compliant, plant-based food ingredients, contributing to both innovation and sustainability in the food industry.
Balancing Conservation and Development: A Policy Framework for Sustainable Forest Management Sun Wei; Yang Xiang; Zhang Li
Journal of Selvicoltura Asean Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i4.1665

Abstract

The interplay between conservation and development poses significant challenges in forest management. Unsustainable practices often lead to biodiversity loss and environmental degradation, highlighting the urgent need for effective policy frameworks that balance these competing interests. Sustainable forest management is essential for preserving ecosystems while supporting economic growth. This research aims to develop a comprehensive policy framework that harmonizes conservation and development goals in forest management. The study seeks to identify strategies that promote sustainable practices, enhance biodiversity, and support local communities' livelihoods. A mixed-methods approach was employed, combining qualitative and quantitative data collection. Case studies from various regions were analyzed to understand existing policies and their impacts on forest management. Stakeholder interviews and surveys were conducted to gather insights on the challenges and opportunities in balancing conservation with development. The findings indicate that successful policy frameworks incorporate multi-stakeholder participation, adaptive management strategies, and comprehensive monitoring systems. The analysis revealed that integrating local knowledge and addressing socio-economic factors are crucial for effective implementation. Case studies demonstrated that successful balance results in improved ecological outcomes and enhanced community well-being. The research underscores the importance of a holistic approach to forest management that aligns conservation and development objectives. By implementing the proposed policy framework, stakeholders can foster sustainable practices that benefit both ecosystems and local communities, ensuring long-term viability and resilience of forest resources.
Development of Quantum Noise-Based Quantum Random Number Generator (QRNG) Yang Xiang; Wang Jing; Sun Wei
Journal of Tecnologia Quantica Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i4.1682

Abstract

The background of this research focuses on the development of a quantum noise-based Quantum Random Number Generator (QRNG) to generate random numbers that are safer and more efficient compared to conventional methods. Quantum fluctuation-based QRNG has the potential to generate more unpredictable numbers, improving security in cryptographic and simulation applications. The purpose of this research is to develop a QRNG system that can generate high-quality random numbers with various experimental settings and conditions. The method used is an experiment measuring quantum fluctuations through a photon detector to generate a random number based on quantum noise, followed by statistical testing to test the quality of the randomness. The results show that quantum noise-based QRNG is able to generate random numbers with better quality than conventional random number generators, with p-values that indicate very high random uncertainty. In addition, these QRNGs can operate at various photon intensities without compromising the random quality produced. The conclusion of this study is that quantum noise-based QRNG offers a safer and more efficient solution in generating random numbers for applications that require high randomness. Further research is needed to improve efficiency and overcome implementation obstacles in the real world.
The Post-Quantum Cryptography Challenge: A Security Analysis of Lattice-Based vs. Code-Based Algorithms Loso Judijanto; Zhou Hui; Sun Wei
Journal of Tecnologia Quantica Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.2794

Abstract

The emergence of large-scale quantum computers poses a critical threat to classical public-key cryptographic systems, prompting the rapid development of post-quantum cryptography as a foundational component of future digital security. Lattice-based and code-based algorithms have become leading candidates due to their strong conjectured resistance to quantum attacks; however, their comparative security characteristics remain insufficiently examined under unified analytical frameworks. This study aims to provide a comprehensive security analysis of lattice-based and code-based post-quantum cryptographic algorithms by evaluating their resilience against known classical and quantum attack vectors. A structured methodological approach is employed, combining complexity-theoretic assessment, parameter-sensitivity evaluation, and simulated attack modeling across representative schemes such as CRYSTALS-Kyber, NTRU, Classic McEliece, and BIKE. The results indicate that lattice-based schemes offer strong security margins under current attack models but exhibit notable sensitivity to parameter misconfiguration and structured lattice weaknesses. Code-based schemes demonstrate exceptional robustness due to the hardness of decoding random linear codes, yet face practical limitations in key size and implementation overhead. The study concludes that both families remain viable for post-quantum standardization, although their security assurances depend heavily on careful parameter selection and continued cryptanalytic scrutiny as quantum hardware evolves.
The Influence of Organizational Culture on the Level of Innovation in Manufacturing Companies Zhang Li; Yang Xiang; Sun Wei
Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v1i5.1710

Abstract

This study examines the influence of organizational culture on the level of innovation in manufacturing companies. Organizational culture plays a crucial role in shaping the behavior of employees and influencing how companies adapt to changes and foster innovation. Despite the growing importance of innovation in maintaining competitiveness in the manufacturing sector, the relationship between organizational culture and innovation has not been fully explored in this context. This research aims to investigate how different dimensions of organizational culture—such as support for risk-taking, communication practices, and employee involvement—affect innovation outcomes in manufacturing firms. A quantitative research design was employed, using surveys distributed to employees in various manufacturing companies across different regions. The survey data were analyzed using statistical methods, including regression analysis, to determine the correlation between organizational culture and innovation levels. The findings indicate that a strong, innovation-supportive organizational culture significantly enhances the innovation capacity of manufacturing companies. Specifically, companies with cultures that promote open communication and risk-taking showed higher levels of innovative output. The study concludes that fostering a culture that values creativity, risk-taking, and collaboration can significantly improve innovation outcomes in manufacturing companies. Future research should further explore the role of leadership in shaping organizational culture and driving innovation within this sector.  
THE USE OF MULTISPECTRAL DRONE IMAGERY AND ARTIFICIAL INTELLIGENCE FOR THE EARLY DETECTION OF LEAF BLIGHT DISEASE IN INDONESIAN RICE PADDIES Sun Wei; Wang Jun; Liu Yang
Techno Agriculturae Studium of Research Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i5.2961

Abstract

Leaf blight disease remains one of the major threats to rice production in Indonesia, causing significant yield losses and threatening national food security. Conventional detection methods rely heavily on manual field inspection, which is time-consuming, labor-intensive, and often ineffective for early-stage identification. Recent advances in multispectral drone imagery and artificial intelligence (AI) offer new opportunities for precision agriculture by enabling rapid, accurate, and large-scale crop health monitoring. However, the practical application of these technologies in Indonesian rice paddies is still limited and requires empirical validation. This study aims to examine the effectiveness of multispectral drone imagery integrated with AI-based classification models for the early detection of leaf blight disease in Indonesian rice fields. The research focuses on improving detection accuracy and supporting timely disease management decisions for farmers and agricultural stakeholders. The study employs an experimental research design using multispectral drone data collected from rice paddies in West Java during the growing season. Vegetation indices such as NDVI and GNDVI were extracted and analyzed using machine learning algorithms, including Random Forest and Convolutional Neural Networks (CNN). Ground truth data were obtained through field observations and laboratory confirmation to validate the model outputs. The results demonstrate that the AI-based model achieved high classification accuracy, exceeding 90% in detecting early-stage leaf blight symptoms. The integration of multispectral data significantly improved detection performance compared to visual RGB imagery alone. The study concludes that multispectral drone imagery combined with AI provides a reliable and efficient approach for early detection of leaf blight disease in rice paddies. This approach has strong potential to support precision agriculture, reduce crop losses, and enhance sustainable rice production in Indonesia.
INTERPRETATION OF DEEP LEARNING MODELS IN NATURAL LANGUAGE PROCESSING FOR MISINFORMATION DETECTION WITH THE EXPLAINABLE AI (XAI) APPROACH mas'ud muhammadiah; Rashid Rahman; Sun Wei
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2104

Abstract

The increasing spread of misinformation through digital platforms has raised significant concerns about its societal impact, particularly in political, health, and social domains. Deep learning models in Natural Language Processing (NLP) have shown high performance in detecting misinformation, but their lack of interpretability remains a major challenge for trust, transparency, and accountability. As black-box models, they often fail to provide insights into how predictions are made, limiting their acceptance in sensitive real-world applications. This study investigates the integration of Explainable Artificial Intelligence (XAI) techniques to enhance the interpretability of deep learning models used in misinformation detection. The primary objective of this research is to evaluate how different XAI methods can be applied to explain and interpret the decisions of NLP-based misinformation classifiers. A comparative analysis was conducted using state-of-the-art deep learning models such as BERT and LSTM on benchmark datasets, including FakeNewsNet and LIAR. XAI methods including SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and attention visualization were applied to analyze model behavior and feature importance. The findings reveal that while deep learning models achieve high accuracy in misinformation detection, XAI methods significantly improve transparency by highlighting influential words and phrases contributing to model decisions. SHAP and LIME proved particularly effective in providing human-understandable explanations, aiding both developers and end-users. In conclusion, incorporating XAI into NLP-based misinformation detection frameworks enhances model interpretability without sacrificing performance, paving the way for more responsible and trustworthy AI deployment in combating online misinformation.
Polymers and Composites for Energy Storage Applications Chen Mei; Wang Jing; Sun Wei
Research of Scientia Naturalis Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i4.1576

Abstract

The increasing demand for efficient energy storage solutions has driven research into polymers and composites. These materials offer unique advantages, such as lightweight properties, flexibility, and tunable conductivity, making them ideal candidates for energy storage applications. The exploration of innovative polymers and composites is essential for improving energy density and cycle life in storage devices. This research aims to evaluate the performance of various polymers and composites in energy storage applications. The focus is on understanding their electrochemical properties and how modifications can enhance their performance in batteries and supercapacitors. A systematic review of recent advancements in polymer and composite materials was conducted, alongside experimental assessments of selected materials. Performance metrics such as conductivity, energy density, and stability were evaluated using electrochemical testing methods, including cyclic voltammetry and galvanostatic charge-discharge tests. The findings indicate that specific polymers and composites exhibit enhanced performance in energy storage applications. Notable improvements in conductivity and energy density were observed, particularly with the incorporation of conductive fillers. Additionally, the stability of the materials under cycling conditions showed promising results, suggesting their potential for practical applications.The research highlights the significant potential of polymers and composites in advancing energy storage technologies. Continued exploration and optimization of these materials can lead to the development of more efficient and durable energy storage solutions, addressing the growing demands for sustainable energy systems.
Co-Authors Agnita Yolanda ahmad ikhwan Benny Novico Zani Chen Mei Elvis Ewane HARMONO HARMONO Ilma Saakinah Tamsil Jung Yuna Kanti Aldino Hadi Kemmala Dewi Khalil Zaman Li Na Lim Haeun Liu Yang Liu Yang Loso Judijanto Mas'ud Muhammadiah Meera Gupta Michael Johnson Muchamad Sobri Sungkar Ngr. Putu Raka Novandra Asta Nopriadi Nopriadi Rashid Rahman Ria Wuri Andary Rustiyana, Rustiyana Ryan Teo Sayed Achmady Suyono Arni Vinto Rizalfi Wang Jing Wang Jun Yang Xiang Zain Nizam Zhang Li Zhou Hui