The Application of Support Vector Machine Method to Analyze the Sentiments of Netizens on Social Media Regarding the Accessibility of Disabilities in Public Spaces
Keywords:
disabilities, infrastructure, social media, support vector machineAbstract
According to the Ministry of Social Affairs of the Republic of Indonesia, based on data from the Central Bureau of Statistics, there will be 22.5 million people with disabilities in Indonesia. As time goes on, it is possible that the number of people with disabilities will increase. So, facilities and infrastructure that are friendly to people with disabilities must be given great attention. Various comments from the public regarding this matter arose on social media, especially Twitter, both positive and negative. There are lot of tweet in Indonesian related to this matter, which will be classified using the Support Vector Machine algorithm with the Radial Basis Function kernel using Grid Search and cross-validation. The parameters used are in the range C, γ and produce a maximum accuracy of the parameter combination, result of this study was aligned with previous research that a good and appropriate parameter combination of C and γ in the RBF kernel SVM method will produce maximum accuracy of the classification results.
References
Borg, A., & Boldt, M. (2020). Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications, 162.
Andry, J. F., Tannady, H., & Gunawan, F. E. (2020). Purchase order information system using feature driven development methodology. International Journal of Advanced Trends in Computer Science and Engineering, 9(2), 1107-1112.
Ceron, A., & Memoli, V. (2016). Flames and Debates: Do Social Media Affect Satisfaction with Democracy? Social Indicators Research, 126(1).
Gunawan, F. E., Andry, J. F., Tannady, H., & Meylovsky, R. (2019). Designing enterprise architecture using togaf framework in meteorological, climatological, and geophysical agency. Journal of Theoretical and Applied Information Technology, 97(20), 2376-2385.
Hadi, I. (2019). The Urgency of Disability Accessibility in Gorontalo District Government Agencies. Al-Himayah, 3.
Hendy, T., Resdiansyah, R., Johanes, F. A., & Rustono, F. M. (2020). Exploring the role of ICT readiness and information sharing on supply chain performance in coronavirus disruptions. Technol. Rep. Kansai Univ, 62, 2581-2588.
Sultana, J., Sultana, N., Yadav, K., & Alfayez, F. (2018). Prediction of Sentiment Analysis on Educational Data based on Deep Learning Approach. 21st Saudi Computer Society National Computer Conference, NCC 2018.
Suryono, S., Utami, E., & Luthfi, E. T. (2018). Sentiment Classification on Twitter with Naive Bayes Classifier. Angkasa: Jurnal Ilmiah Bidang Teknologi, 10(1).
Tannady, H., Andry, J. F., Gunawan, F. E., & Mayseleste, J. (2020). Enterprise architecture artifacts enablers for it strategy and business alignment in forwarding services. International Journal of Advanced Trends in Computer Science and Engineering, 9(2), 1465-1472.
Tannady, H., Andry, J. F., Sudarsono, B. G., & Krishartanto, Y. (2020). Enterprise architecture using Zachman framework at paint manufacturing company. Technol. Reports Kansai Univ, 62(4), 1869-1883.
Published
How to Cite
Issue
Section
Copyright (c) 2023 JISTE Journal of Information System, Technology and Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
