DEVELOPMENT OF DATACRAFT APPLICATION FOR SOCIAL MEDIA SENTIMENT ANALYSIS AS AN EFFORT TO EMPOWER THE COMMUNITY DIGITALLY
DOI:
https://doi.org/10.71154/asmara.v2i1.48Keywords:
sentiment analysis, DataCraft, machine learning, digital empowerment, community serviceAbstract
The rapid development of social media creates enormous volumes of text data, requiring sentiment analysis tools that are easily accessible to the public. The purpose of this community service is to develop the DataCraft application as a free web platform for social media sentiment analysis using machine learning algorithms. The implementation method includes a 1-month data collection phase and 1.5 months application development using Python Flask, Naive Bayes, and Support Vector Machine (SVM) technologies. The application is equipped with data exploration, automatic preprocessing, modeling, and result visualization features. Test results show Naive Bayes accuracy reaches 77.25% with a user-friendly interface. The application successfully provides free access to students, researchers, and practitioners to perform sentiment analysis without requiring high programming skills. The impact of community service shows an increase in digital literacy capabilities of the community in analyzing public opinion on social media. DataCraft is available open-source on GitHub with complete tutorials to facilitate wider adoption.
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References
Ahmad, R., & Putri, S. (2022). Machine learning applications in social media sentiment analysis. Journal of Digital Innovation, 8(2), 45-62.
Chen, L., Wang, M., & Zhang, Y. (2019). Social media sentiment analysis: Methods and applications. International Journal of Information Technology, 15(3), 123-145.
Dewi, K., Rahman, A., & Sari, P. (2021). Open-source tools for natural language processing in Indonesian language. Computational Linguistics Research, 7(4), 78-95.
Hidayat, M., & Kusuma, W. (2023). Comparative analysis of machine learning algorithms for text classification. Applied Computer Science, 19(1), 34-51.
Indra, F., Novita, R., & Pratama, D. (2020). Web-based application development using Flask framework. Software Engineering Journal, 12(3), 156-173.
Liu, B. (2020). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (2nd ed.). Cambridge University Press.
Ngiliyun, A., Rosidin, R., & Nugraha, D. (2024). The application of RSA cryptography to prevent counterfeiting in the book publishing industry. TECHNOVATAR Jurnal Teknologi, Industri, Dan Informasi, 2(4), 111-128. https://doi.org/10.61434/technovatar.v2i4.247
Permana, I. S., Ngiliyun, A., & Ahmad Subagia, H. (2023). Upaya meningkatkan etika bermedia digital bagi siswa di SMP PGRI Karangampel. ADIMA Jurnal Awatara Pengabdian Kepada Masyarakat, 1(1), 25-30. https://doi.org/10.61434/adima.v1i1.133
Putri, A. S., & Handayani, M. (2023). User interface design principles for data analysis applications. Human-Computer Interaction Studies, 11(2), 89-107.
Rahman, D., & Sari, L. (2021). User-centered design approach in web application development. Design & Technology Journal, 9(4), 201-218.
Sari, M., Pratama, B., & Widodo, S. (2021). Digital literacy enhancement through open-source software adoption. Educational Technology Research, 16(3), 145-162.
Singh, P., & Kumar, A. (2020). Support Vector Machine and Naive Bayes performance comparison in sentiment analysis. Machine Learning Applications, 14(1), 67-84.
Sutanto, H., Wibowo, A., & Maharani, D. (2022). Natural language processing techniques for Indonesian text preprocessing. Language Technology Journal, 8(3), 112-129.
Wijaya, S., Kurniawan, B., & Lestari, N. (2022). Implementation of NLTK and Sastrawi libraries for Indonesian text processing. Text Mining Research, 6(2), 78-95.
Yusuf, M., Anggraeni, P., & Firmansyah, R. (2023). Data visualization techniques in machine learning applications. Visual Analytics Journal, 5(1), 23-40.
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