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p-Index From 2020 - 2025
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All Journal Journal of Computer Science Advancements Techno Agriculturae Studium of Research
Lucas, Laura
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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.  
Vertical Farming Innovation in Urban Netherlands: Sustainable Solutions with Modern Hydroponics Nina, Nening; Lucas, Laura; Sridar, Katja
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

Abstract

In the era of globalization and rapid population growth, agricultural land in urban areas is increasingly limited. As a country with a high population density, the Netherlands has adopted vertical farming innovations using modern hydroponics as an alternative to increasing the efficient use of space and resources. This research aims to evaluate the effectiveness of hydroponic vertical farming in urban areas in the Netherlands as a sustainable solution. Specifically, this research focuses on measuring the increase in productivity and reduction in environmental impact compared to traditional farming methods. This research uses a quantitative approach with an experimental design. Data were collected from several vertical farming locations in the urban Netherlands. Variables measured include plant productivity, water use, and carbon emissions. Data analysis was carried out using descriptive and inferential statistics. Results show that vertical farming with modern hydroponics in Dutch urban areas increases crop productivity by 40% and reduces water use by 60% compared to traditional agriculture. In addition, energy use is significantly reduced, which contributes to reduced carbon emissions. Vertical farming using modern hydroponic systems in Dutch urban areas offers an efficient, sustainable solution. By increasing productivity and reducing resource use, this technology could answer future agricultural challenges in dense urban areas. Further research is needed to optimize this technology and adapt it to various geographic and climatic conditions.
Co-Authors Nina, Nening Qingjun, Chu Sahirin, Rohmat Sridar, Katja Usup, Usup