Classification of Community Complaints  Against Public Services on Twitter

Muqorobin Muqorobin; Siti Rokhmah; Isnawati Muslihah; Nendy Akbar Rozaq Rais

doi:10.29040/ijcis.v1i1.6

Classification of Community Complaints Against Public Services on Twitter

Muqorobin Muqorobin, Siti Rokhmah, Isnawati Muslihah, Nendy Akbar Rozaq Rais

Abstract

Abstract— Information on public services is an important part of increasing community satisfaction with government policies. Complaints and Complaints of the community become mediators to improve public services according to community needs.Twitter is one of the most widely used social media in the community to post activities, experiences, and complaints about public services through the internet easily and realtime.The amount of information on Twitter is mixed between satisfaction and extensibility of public services, making it difficult for the government to make decisions in public policy. The role of Big Data can be a solution to classifying data to predict satisfaction or extensibility of public services with parameters: markets, transportation and hospitals.Data sources taken from Twitter are 700 data texts. The twitter classification of public service complaints is built using the Naïve Bayes Algorithm Method, because the algorithm can classify based on probability values. Text processing is done by filtering text and selecting text to be ordered.The results of this study indicate that the Naïve Bayes Method is able to properly classify public service complaints based on 3 parameters, transportation, markets and hospitals. System testing using 700 data obtained the best results accuracy value: 86%, and precision: 72%, recall 81% and f-measure: 83%.

Full Text:

PDF

References

1. B. Kurniawan, M. A. Fauzi, and A. W. Widodo, “Twitter News ClassificationUsed Metode Improved Naïve Bayes,” vol. 1, no. 10, pp. 1193–1200, 2017.

2. K. Yu, “Toward an Incremental Democracy and Governance : Chinese Theories and Assessment Criteria,” New Polit. Sci., vol. 24, no. 2, pp. 181–199, 2002.

3. V. Effendy, A. Novantirani, and M. K. Sabariah, “Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method,” Intl. J. ICT, vol. 2, no. 1, pp. 57–66, 2016.

4. A. Rane and A. Kumar, “Sentiment Classification System of Twitter Data for US Airline Service Analysis,” Proc. - Int. Comput. Softw. Appl. Conf., vol. 1, pp. 769–773, 2018.

5. A. M. Qamar, S. A. Alsuhibany, and S. S. Ahmed, “Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 1, pp. 395–401, 2017.

6. X. Li, Z. Ma, and H. Chen, “QODM: A query-oriented data modeling approach for NoSQL databases,” in Proceedings - 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014, 2014, pp. 338–345.

7. S. K. Kim, M. J. Park, and J. J. Rho, “Effect of the Government ’ s Use of Social Media on the Reliability of the Government : Focus on Twitter,” no. April 2015, pp. 37–41, 2015.

8. L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, “Target-dependent Twitter Sentiment Classification,” pp. 151–160, 2011.

9. K. Lee, D. Palsetia, R. Narayanan, M. A. Patwary, A. Agrawal, and A. Choudhary, “Twitter Trending Topic Classification,” pp. 251–258, 2011.

10. R. Amalia, M. A. Bijaksana, and D. Darmantoro, “A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter,” in International Conference on Computing and Applied Informatics, 2017.

11. Abdullah, Robi W., et al. "Keamanan Basis Data pada Perancangan Sistem Kepakaran Prestasi Sman Dikota Surakarta." Creative Communication and Innovative Technology Journal, vol. 12, no. 1, 2019, pp. 13-21.

12. Muqorobin, M., Apriliyani, A., & Kusrini, K. (2019). Sistem Pendukung Keputusan Penerimaan Beasiswa dengan Metode SAW. Respati, 14(1).

13. Muqorobin, M., Hisyam, Z., Mashuri, M., Hanafi, H., & Setiyantara, Y. (2019). Implementasi Network Intrusion Detection System (NIDS) Dalam Sistem Keamanan Open Cloud Computing. Majalah Ilmiah Bahari Jogja, 17(2), 1-9.

14. K. Kusrini, E. T. Luthfi, M. Muqorobin and R. W. Abdullah, "Comparison of Naive Bayes and K-NN Method on Tuition Fee Payment Overdue Prediction," 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, 2019, pp. 125-130, doi: 10.1109/ICITISEE48480.2019.9003782.

DOI: https://doi.org/10.29040/ijcis.v1i1.6