Articles
<![CDATA[Islamic Journalism and the Challenges of Objectivity ]]>
<![CDATA[Ilyas, Sanusi]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Wei, Sun]]>
<![CDATA[ Journal International Dakwah and Communication Vol. 4 No. 2 (2024) ]]>
Publisher : <![CDATA[Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia. ]]>
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DOI: 10.55849/jidc.v4i2.763
<![CDATA[Islamic journalism operates within a framework that integrates ethical and religious principles, presenting distinct challenges in maintaining objectivity. The tension between upholding journalistic neutrality and adhering to Islamic ethical guidelines raises questions about bias, credibility, and professional standards in Islamic media practices. This study aims to analyze the complexities of objectivity in Islamic journalism by examining its theoretical foundations, ethical boundaries, and practical applications in contemporary media landscapes. Employing a qualitative approach, this research utilizes content analysis and in-depth interviews with journalists from various Islamic media outlets. The findings reveal that while Islamic journalism strives for truthfulness and fairness, it often navigates ideological influences and societal expectations that shape reporting styles. Additionally, structural limitations, editorial policies, and political factors further challenge the realization of absolute objectivity. This study concludes that while complete neutrality may be unattainable, Islamic journalism can enhance credibility by promoting balanced reporting, ethical transparency, and adherence to professional journalism standards. These insights contribute to the discourse on media ethics, highlighting the need for frameworks that align Islamic values with universal journalistic principles. ]]>
<![CDATA[Employee Wellbeing and Work Productivity: The Role of Psychological Capital ]]>
<![CDATA[Azizah, Siti Nur]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Hui, Zhou]]>
<![CDATA[ Journal Markcount Finance Vol. 3 No. 1 (2025) ]]>
Publisher : <![CDATA[Yayasan Pendidikan Islam Daarut Thufulah ]]>
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DOI: 10.70177/jmf.v3i1.2132
<![CDATA[In the contemporary workplace, employee wellbeing has emerged as a critical factor influencing work productivity. Psychological capital (PsyCap), encompassing hope, efficacy, resilience, and optimism, is posited as a key driver in enhancing both wellbeing and productivity. Despite its potential, the role of PsyCap in mediating the relationship between employee wellbeing and productivity remains underexplored. This study aims to investigate the role of psychological capital in linking employee wellbeing to work productivity, providing insights into how organizations can leverage PsyCap to foster a productive workforce. A quantitative approach was employed, utilizing structured surveys distributed to 350 employees across various industries. Data were analyzed using structural equation modeling (SEM) to examine the mediating role of PsyCap. The findings reveal that psychological capital significantly mediates the relationship between employee wellbeing and work productivity. Employees with higher levels of PsyCap reported greater wellbeing and demonstrated enhanced productivity. Hope and resilience emerged as the most influential components of PsyCap in this context. This study underscores the importance of psychological capital as a pivotal mechanism connecting employee wellbeing to productivity. Organizations are encouraged to invest in PsyCap development programs to cultivate a resilient and optimistic workforce, ultimately driving organizational success.]]>
<![CDATA[Multimodal Sentiment Analysis in Indonesian: A Comparative Study of Deep Learning Models for Hate Speech Detection on Social Media ]]>
<![CDATA[Muhammadiyah, Mas’ud]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Na, Li]]>;
<![CDATA[Nishida, Daiki]]>;
<![CDATA[Prayudani, Santi]]>
<![CDATA[ Journal International of Lingua and Technology Vol. 4 No. 1 (2025) ]]>
Publisher : <![CDATA[Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia. ]]>
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DOI: 10.55849/jiltech.v4i1.824
<![CDATA[With the rapid expansion of social media, the prevalence of hate speech has become a critical issue, particularly in the context of Indonesian language and culture. The detection of hate speech in social media platforms is a complex task due to the multimodal nature of online communication, where text, images, and videos are often combined to express sentiments. This study aims to explore and compare deep learning models for multimodal sentiment analysis, focusing on their effectiveness in detecting hate speech in Indonesian social media content. By analyzing both textual and visual data, the study seeks to enhance the accuracy of sentiment classification, specifically identifying instances of hate speech. The research employs several state-of-the-art deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-based models, to perform sentiment analysis on a multimodal dataset. The dataset includes text and images from Indonesian social media posts, labeled for hate speech detection. The results show that multimodal models outperform text-only models, with the Transformer-based model yielding the highest accuracy and F1-score in detecting hate speech. The inclusion of visual data significantly improved the model’s ability to classify complex and subtle expressions of hate speech. This study concludes that multimodal deep learning models offer a promising solution for detecting hate speech in Indonesian social media, with implications for better content moderation and online safety.]]>
<![CDATA[Performance Analysis of Cloud Computing Systems in Collaborative Software Development Environments ]]>
<![CDATA[Li, Zhang]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Vandika, Arnes Yuli]]>
<![CDATA[ Journal of Moeslim Research Technik Vol. 1 No. 6 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/technik.v1i6.1562
<![CDATA[The rise of cloud computing has transformed software development, enabling collaborative environments that enhance productivity and efficiency. However, the performance of cloud computing systems in supporting collaborative software development remains an area of active research, with various factors influencing effectiveness. This study aims to analyze the performance of cloud computing systems in collaborative software development environments. The focus is on identifying key performance metrics and their impact on team productivity and project outcomes. A mixed-methods approach was employed, combining quantitative performance metrics and qualitative surveys from development teams using cloud-based tools. Key metrics analyzed included system uptime, response time, and resource utilization. Surveys gathered insights on user satisfaction and perceived efficiency improvements. The findings reveal that cloud computing systems significantly enhance collaboration among software development teams. Metrics indicated an average system uptime of 99.5%, with response times averaging under 200 milliseconds. Survey results showed that 85% of participants reported increased productivity when using cloud-based tools compared to traditional methods. The research concludes that cloud computing systems provide substantial performance advantages in collaborative software development environments. These systems facilitate better communication, resource sharing, and project management, ultimately leading to improved project outcomes. Future research should explore the long-term effects of cloud computing on software development practices and its implications for team dynamics.]]>
<![CDATA[Automated Detection of Road Surface Defects Using UAVs and Convolutional Neural Networks ]]>
<![CDATA[Zahir, Roya]]>;
<![CDATA[Khan, Jamil]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Shofiah, Siti]]>
<![CDATA[ Journal of Moeslim Research Technik Vol. 2 No. 3 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/technik.v2i3.2349
<![CDATA[This study presents a novel approach to the automated detection of road surface defects using Unmanned Aerial Vehicles (UAVs) and advanced image processing. The research background highlights the critical need for efficient and safe road infrastructure maintenance. Traditional methods, which rely on manual visual inspections, are often time-consuming, expensive, and expose inspectors to traffic risks. The primary objective is to design and validate an automated system for identifying and classifying various road surface defects, such as potholes, cracks, and rutting. The system aims to leverage aerial imagery captured by UAVs and process it with a Convolutional Neural Network (CNN). The research seeks to demonstrate a solution that is faster, more accurate, and safer than manual inspection methods, paving the way for proactive road maintenance. The research methodology involves three key stages: data acquisition, model development, and validation. High-resolution images of various road defects are captured using a UAV. These images are then used to train a custom-designed CNN model. The model is trained to recognize and classify different types of defects with high precision. The results indicate that the combination of UAVs and CNNs is a robust and effective solution for road monitoring. The conclusion is that this automated system provides a scalable, safe, and highly accurate method for road surface defect detection. ]]>
<![CDATA[Balancing Conservation and Development: A Policy Framework for Sustainable Forest Management ]]>
<![CDATA[Wei, Sun]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Li, Zhang]]>
<![CDATA[ Journal of Selvicoltura Asean Vol. 1 No. 4 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/jsa.v1i4.1665
<![CDATA[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.]]>
<![CDATA[Development of Quantum Noise-Based Quantum Random Number Generator (QRNG) ]]>
<![CDATA[Xiang, Yang]]>;
<![CDATA[Jing, Wang]]>;
<![CDATA[Wei, Sun]]>
<![CDATA[ Journal of Tecnologia Quantica Vol. 1 No. 4 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/quantica.v1i4.1682
<![CDATA[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.]]>
<![CDATA[The Influence of Organizational Culture on the Level of Innovation in Manufacturing Companies ]]>
<![CDATA[Li, Zhang]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Wei, Sun]]>
<![CDATA[ Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 5 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/innovatsioon.v1i5.1710
<![CDATA[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. ]]>
<![CDATA[Greenhouse Technology Innovations for Sustainable Agriculture in the United Kingdom ]]>
<![CDATA[Li, Zhang]]>;
<![CDATA[Xiang, Yang]]>;
<![CDATA[Yang, Liu]]>;
<![CDATA[Nampira, Ardi Azhar]]>
<![CDATA[ Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/agriculturae.v2i1.1993
<![CDATA[Greenhouse technology is an important innovation in facing the challenges of sustainable agriculture in the UK, especially in the face of climate change and increasing food needs. This research aims to explore the application of advanced technologies in greenhouses, such as automation sensors, hydroponics, aquaponics, and renewable energy, as well as their impact on agricultural productivity and sustainability. Descriptive-qualitative research methods are used to gain insights from farmers and experts in the field of agricultural technology, through interviews and direct observations. The results showed a significant improvement in resource use efficiency, with a reduction in water use of up to 50% and an increase in crop yields of up to 30%. The adoption of renewable energy in greenhouses also plays a role in reducing carbon emissions and operational costs. In conclusion, greenhouse technology innovation has the potential to be an important solution to achieving sustainable agriculture in the UK, but more research is needed to evaluate the long-term impact on the environment.]]>
<![CDATA[Innovations in Bioremediation: Harnessing Microbial Power to Clean Up Pollution ]]>
<![CDATA[Xiang, Yang]]>;
<![CDATA[Wei, Sun]]>;
<![CDATA[Ewane, Elvis]]>
<![CDATA[ Research of Scientia Naturalis Vol. 2 No. 2 (2025) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v2i2.2008
<![CDATA[Pollution poses a significant threat to ecosystems and human health, prompting the need for effective remediation strategies. Bioremediation, which utilizes microorganisms to degrade environmental pollutants, has emerged as a promising approach to address this challenge. This study aims to explore recent advancements in bioremediation technologies, focusing on the role of specific microbial communities in the degradation of various pollutants, including heavy metals, hydrocarbons, and pesticides. The research seeks to identify effective microbial strategies and their applications in real-world scenarios. A comprehensive literature review was conducted, analyzing recent studies on microbial bioremediation techniques. Laboratory experiments were performed to evaluate the degradation rates of selected pollutants by specific microbial strains. Case studies of successful bioremediation projects were also included to illustrate practical applications. Findings indicate that innovative microbial techniques, such as genetically engineered strains and bioaugmentation, significantly enhance the degradation of pollutants. Successful case studies demonstrated substantial reductions in pollutant concentrations, showcasing the efficacy of microbial bioremediation in various environments. This research highlights the potential of harnessing microbial power for effective pollution cleanup.]]>
