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All Journal Journal of Computer Science Advancements Techno Agriculturae Studium of Research
Qingjun, Chu
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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Sentiment Analysis on Social Media Using Data Mining for Mapping Community Satisfaction Usup, Usup; Sahirin, Rohmat; Lucas, Laura; Qingjun, Chu
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1536

Abstract

Social media has become a significant platform for individuals to express opinions, including satisfaction and dissatisfaction with services and policies, making it a valuable source of community sentiment data. Understanding public sentiment can assist policymakers and organizations in responding to community needs effectively. This study aims to conduct sentiment analysis on social media using data mining techniques to map community satisfaction levels. By analyzing sentiment patterns, this research seeks to provide actionable insights for improving public services and enhancing community engagement. The research applies data mining methodologies, including text mining and machine learning algorithms, to analyze posts and comments collected from various social media platforms. Sentiment classification was performed using natural language processing (NLP) and a supervised machine learning approach to categorize sentiments as positive, neutral, or negative. The model was trained on a large dataset and validated to ensure accuracy in sentiment detection. Results indicate that social media sentiment analysis can reliably reflect community satisfaction trends, with findings showing 70% positive, 15% neutral, and 15% negative sentiments regarding local services. The study concludes that data mining for sentiment analysis provides a robust method for assessing community satisfaction on social media, offering a real-time understanding of public opinion. By implementing this approach, organizations and policymakers can identify areas of improvement and proactively address community concerns, ultimately fostering a responsive and community-centered approach to public service.  
Mapping and Remote Sensing Technology for Agricultural Land Management in China Yonglong, Shi; Qingjun, Chu; Guanghui, Benxiang
Techno Agriculturae Studium of Research Vol. 1 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v1i2.957

Abstract

Agricultural land management in China faces significant challenges due to rapid urbanization, climate change, and the need for sustainable practices. Advanced technologies such as mapping and remote sensing offer promising solutions to enhance agricultural productivity and sustainability. These technologies provide precise data for monitoring crop health, soil conditions, and land use, enabling better decision-making and resource management. This study aims to evaluate the effectiveness of mapping and remote sensing technology in improving agricultural land management in China. The research assesses how these technologies can enhance crop monitoring, optimize resource use, and support sustainable farming practices. A mixed-methods approach was employed, combining quantitative data from satellite imagery and field surveys with qualitative insights from interviews with farmers and agricultural experts. Satellite imagery was analyzed to monitor crop health, soil moisture, and land use patterns. Field surveys were conducted to validate the remote sensing data. Interviews with farmers and experts provided additional insights into these technologies' practical benefits and challenges. The findings indicate that mapping and remote sensing technology significantly improve agricultural land management. Crop health monitoring through remote sensing showed a 25% increase in accuracy compared to traditional methods. Optimized resource use was observed, with a 20% reduction in water and fertilizer usage. Land use patterns were more efficiently managed, leading to better crop rotation and soil conservation practices. Farmers reported enhanced decision-making capabilities and improved crop yields. Mapping and remote sensing technology substantially benefit agricultural land management in China. The increased accuracy in crop monitoring and optimized resource use contribute to higher productivity and sustainability. Further research and investment in these technologies are recommended to maximize their potential and support sustainable agricultural practices.
Co-Authors Guanghui, Benxiang Lucas, Laura Sahirin, Rohmat Usup, Usup Yonglong, Shi