Unsupervised Twitter Sentiment Analysis on The Revision of Indonesian Code Law and the Anti-Corruption Law using Combination Method of Lexicon Based and Agglomerative Hierarchical Clustering

  • Nur Restu Prayoga family
  • Tresna Maulana Fahrudin Universitas Narotama https://orcid.org/0000-0002-9895-2442
  • Made Kamisutara Universitas Narotama
  • Angga Rahagiyanto Department of Medical Records, Politeknik Negeri Jember
  • Tahegga Primananda Alfath Faculty of Law, Universitas Narotama
  • Latipah Faculty of Computer Science, Universitas Narotama
  • Slamet Winardi Faculty of Computer Science, Universitas Narotama
  • Kunto Eko Susilo Faculty of Computer Science, Universitas Narotama
Keywords: Tweet, Law Revision, Sentiment Analysis, Clustering, Agglomerative Hierarchical Clustering

Abstract

The rejection on ratification of the revision of Indonesian Code Law or known as RKUHP and Corruption Law raises several opinions from various perspectives in social media. Twitter as one of many platforms affected, has more than 19.5 million users in Indonesia. Twitter is one of many social media in Indonesia where people can share their views, arguments, information, and opinions from all points of view. Since Twitter has a great diversity of users, it needs a system which is designed to determine the opinion tendency towards the problems or objects. The purpose of this study is to analyze the sentiment of Twitter users' tweets to reject the revision of the Law whether they have positive or negative sentiments using the Agglomerative Hierarchical Clustering method. The data that being used in this study were obtained from the results of crawling tweets based on hashtag (#) (#ReformasiDikorupsi). The next stage is pre-processing which consists of case folding, tokenizing, cleansing, sanitizing, and stemming. The extraction features Lexicon Based and Term Frequency (TF) which performs the process automatically. In the clustering stage, two clusters use three approaches; single linkage, complete linkage and average linkage. In the accuracy calculation phase, the writer uses the error ratio, confusion matrix, and silhouette coefficient. Therefore, the results are quite good. From 2408 tweets, the highest accuracy results are 61.6%.

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Author Biographies

Tresna Maulana Fahrudin, Universitas Narotama

I am Tresna Maulana Fahrudin, S.ST., M.T. Received Bachelor and Applied Master Degree from Politeknik Elektronika Negeri Surabaya. Now, I am lecturer at Universitas Narotama Surabaya (2018-now), join with Study Program of  Informatics Engineering. My research is related to data mining, machine learning, metaheuristic/swarm intelligence, big data&text analytics, and data warehouse&business intelligence.

Made Kamisutara, Universitas Narotama

My name is Made Kamisutara, I am head of study program at Universitas Narotama. My research is related to information system, IT strategic, Early warning system for health, and also IT analysis of governance.

Angga Rahagiyanto, Department of Medical Records, Politeknik Negeri Jember

Sensor,  Human-Computer Interaction, Data Mining

Tahegga Primananda Alfath, Faculty of Law, Universitas Narotama

Analisis Peraturan Perundang-undangan dan Kelembagaan Negara

Latipah, Faculty of Computer Science, Universitas Narotama

Sistem Penunjang Keputusan, Data Mining

Slamet Winardi, Faculty of Computer Science, Universitas Narotama

Internet of Things

Kunto Eko Susilo, Faculty of Computer Science, Universitas Narotama

Instrumentasi dan Kontrol

References

Anggraini, N., & Suroyo, H, Comparison of Sentiment Analysis against Digital Payment “T-cash and Go-pay” in Social Media Using Orange Data Mining, Journal of Information Systems and Informatics, https://doi.org/10.33557/journalisi.v1i2.21, 2019. DOI: https://doi.org/10.33557/journalisi.v1i2.21

Luqyana, W. A., Cholissodin, I., & Perdana, R. S, Analisis Sentimen Cyberbullying Pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine, Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2018.

M. Unnisa, A. Ameen, and S. Raziuddin, Opinion Mining on Twitter Data using Unsupervised Learning Technique, Int. J. Comput. Appl., vol. 148, no. 12, pp. 12–19, 2016. DOI: https://doi.org/10.5120/ijca2016911317

Wang, B., Liakata, M., Zubiaga, A., & Procter, R, A Hierarchical Topic Modelling Approach For Tweet Clustering, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017. DOI: https://doi.org/10.1007/978-3-319-67256-4_30

Prasetyo, E, Data Mining: Konsep dan Aplikasi Menggunakan Matlab, Andi (Yogyakarta), 2012.

Y. Y. Yang dan F. Zhon, Microblog Sentiment Analysis Algorithm Research and Implementation, 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science, pp. 288-291, 2015. DOI: https://doi.org/10.1109/DCABES.2015.79

G. A. Buntoro, Sentiment Analysis Candidates of Indonesian Presiden 2014 with Five Class Attribute, International Journal of Computer Applications (0975 –8887), Volume 136 –No.2, 2016. DOI: https://doi.org/10.5120/ijca2016908288

Desai, R. D., Sentiment Analysis of Twitter Data, Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018, https://doi.org/10.1109/ICCONS.2018.8662942, 2019. DOI: https://doi.org/10.1109/ICCONS.2018.8662942

Rustiana, D., & Rahayu, N, Analisis Sentimen Pasar Otomotif Mobil, Jurnal SIMETRIS, 8(1), pp. 113–120, 2017. DOI: https://doi.org/10.24176/simet.v8i1.841

Buntoro, G. A, Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter, Integer Journal Maret, 2017.

Nugroho, G. A. P., Analisis Sentimen Data Twitter Menggunakan K-Means Clustering, 2016.

Bakshi, R. K., Kaur, N., Kaur, R., & Kaur, G, Opinion Mining And Sentiment Analysis, Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016, https://doi.org/10.1561/1500000011, 2016. DOI: https://doi.org/10.1561/1500000011

Cahyo Ryan Dwi, et al, Deteksi dan Validasi Informasi Gempa Secara Real-Time Berbasis Social Sensor dengan Twitter, JURNAL TEKNIK POMITS Vol. 2, No. 1, 2014.

Darma, I. M. B. S., Penerapan Sentimen Analisis Acara Televisi Pada Twitter Menggunakan Support Vector Machine dan Algoritma Genetika sebagai Metode Seleksi Fitur, 2017.

Indraloka, D. S., & Santosa, B, Penerapan Text Mining untuk Melakukan Clustering Data Tweet Shopee Indonesia, Jurnal Sains Dan Seni ITS, https://doi.org/10.12962/j23373520.v6i2.24419, 2017. DOI: https://doi.org/10.12962/j23373520.v6i2.24419

Ma, B., Yuan, H., & Wu, Y, Exploring Performance Of Clustering Methods On Document Sentiment Analysis, Journal of Information Science, 2017.

E. W. Pamungkas and D. G. P. Putri, An experimental study of lexicon-based sentiment analysis on Bahasa Indonesia, Proc. - 2016 6th Int. Annu. Eng. Semin. Ina. 2016, pp. 28–31, 2017.

Bouguettaya, A., Yu, Q., Liu, X., Zhou, X., & Song, A, Efficient Agglomerative Hierarchical Clustering, Expert Systems with Applications, https://doi.org/10.1016/j.eswa.2014.09.054, 2015. DOI: https://doi.org/10.1016/j.eswa.2014.09.054

T. M. Fahrudin, I. Syarif, and A. R. Barakbah, Data Mining Approach for Breast Cancer Patient Recovery, Emit. Int. J. Eng. Technol., vol. 5, no. 1, pp. 36–71, 2017. DOI: https://doi.org/10.24003/emitter.v5i1.190

Published
2020-06-02
How to Cite
Prayoga, N. R., Tresna Maulana Fahrudin, Made Kamisutara, Rahagiyanto, A., Primananda Alfath, T., Latipah, Winardi, S., & Susilo, K. E. (2020). Unsupervised Twitter Sentiment Analysis on The Revision of Indonesian Code Law and the Anti-Corruption Law using Combination Method of Lexicon Based and Agglomerative Hierarchical Clustering. EMITTER International Journal of Engineering Technology, 8(1), 200-220. https://doi.org/10.24003/emitter.v8i1.477
Section
Articles