• Lalu Mutawalli STMIK Lombok
  • Mohammad Taufan Asri Zaen STMIK Lombok
  • Wire Bagye STMIK Lombok


In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.


[1] Oracle, “The Definition of Big Data,” 2014. [Online]. Available: https://www.oracle.com/big-data/guide/what-is-big-data.html.
[2] G. Szabo, G. Polatkan, O. Boykin, and A. Chalkiopoulos, Social Media Data Mining and Analytics. Indianapolis: Wiley, 2019.
[3] Ri. S. I. Nissa and S. Anggreni, “Kasus Penusukan Wiranto, Butuh Berapa Lama Luka Tusuk Sembuh Total,” Suara, 2019. [Online]. Available: https://www.suara.com/health/2019/10/11/142753/kasus-penusukan-wiranto-butuh-berapa-lama-luka-tusuk-sembuh-total.
[4] C. G. Reddick, A. Takeoka, and A. Ojo, “A social media text analytics framework for double-loop learning for citizen-centric public services : A case study of a local government Facebook use,” Gov. Inf. Q., vol. 34, no. 1, pp. 110–125, 2017.
[5] T. Aleti, J. I. Pallant, A. Tuan, and T. Van Laer, “ScienceDirect Tweeting with the Stars : Automated Text Analysis of the Effect of Celebrity Social Media Communications on Consumer Word of Mouth,” J. Interact. Mark., vol. 48, no. 1, pp. 17–32, 2019.
[6] A. Sun, E. Lim, and Y. Liu, “On strategies for imbalanced text classi fi cation using SVM : A comparative study,” Decis. Support Syst., vol. 48, no. 1, pp. 191–201, 2009.
[7] C. Zhou, C. Sun, Z. Liu, and F. C. M. Lau, “A C-LSTM Neural Network for Text Classification,” Smantic Scholar, 2015. [Online]. Available: https://www.semanticscholar.org/paper/A-C-LSTM-Neural-Network-for-Text-Classification-Zhou-Sun/10f62af29c3fc5e2572baddca559ffbfd6be8787. [Accessed: 26-Nov-2019].
[8] X. Zhang, J. Zhao, and Y. Lecun, “Character-level Convolutional Networks for Text,” Computer Science Machine Learning, 2015. [Online]. Available: https://arxiv.org/abs/1509.01626. [Accessed: 10-Nov-2019].
[9] G. Feng, J. Guo, B. Jing, and T. Sun, “Feature subset selection using naive Bayes for text classification,” Pattern Recognit. Lett., vol. 65, no. 1, pp. 109–115, 2015.
[10] W. Heyong and H. Ming, “Supervised Hebb rule based feature selection for text classification,” Inf. Process. Manag., vol. 56, no. October 2018, pp. 167–191, 2019.
[11] Y. Data and D. Sarkar, Text Analytics with Python. Bangalore: apress, 2016.
[12] M. J. . Betty and G. S. Linoff, Data Mining Techniques For Marketing, Sales, and Customer Relationship Management, Second Edi. Indianapolis: Wiley Publishing, 2004.
[13] J. Han and M. Kamber, Data Mining Concept and Technique. San Fransico: Morgan Kaufman, 2006.
[14] E. T. Qasthari, “Teknik Pengukuran : Metode Klasifikasi Support vector machine ( SVM ) pada Data Pengukuran,” github, 2017. [Online]. Available: https://eufat.github.io/docs/teknik-pengukuran-2.pdf. [Accessed: 20-Nov-2019].
[15] R. Diani, U. N. Wisesty, and A. Aditsania, “Analisis Pengaruh Kernel Support vector machine ( SVM ) pada Klasifikasi Data Microarray untuk Deteksi Kanker,” J. Comput., vol. 2, no. 1, pp. 109–118, 2017.
[16] E. Prasetyo, Data Mining: Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Andi, 2012.
[17] Imtihan, K., Hadawiyah, R., & Lombok, H. A. S. (2018). Sistem Informasi Penggajian Guru Honorer Menggunakan Konsep Agile Software Development dengan Metodologi Extreme Programming (XP) pada SMK Bangun Bangsa. IJNS-Indonesian Journal on Networking and Security, 7(2).
How to Cite
MUTAWALLI, Lalu; ZAEN, Mohammad Taufan Asri; BAGYE, Wire. KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto). Jurnal Informatika dan Rekayasa Elektronik, [S.l.], v. 2, n. 2, p. 43 - 51, dec. 2019. ISSN 2620-6900. Available at: <http://e-journal.stmiklombok.ac.id/index.php/jire/article/view/117>. Date accessed: 15 july 2020. doi: https://doi.org/10.36595/jire.v2i2.117.