Sentiment Analysis On Police Brigadier Shooting Case Using K-Means Clustering
Kata Kunci:
Sentiment analysis , Twitter, Topic modeling , Latent dirichlect allocation , K-means clustering.Abstrak
As a medium that can be used to convey public criticism and aspirations in real time, Twitter is used as a data collection source using crawling techniques, to analyze public sentiment or response to the police brigadier shooting case. Latent dirichlet al-location (LDA) is used to determine the topics that appear in each of the collected tweets, and then used as a feature in grouping the contents of the tweets based on their respective sentiment values. The results of clustering using the k-means clustering algorithm obtained are: 11.9% of netizens gave a positive response, 18.9% of netizens gave a neutral response and 69.2% of netizens gave a negative response to the case. Thus, from the results of this study it can be concluded that netizens tend to give a negative response or reaction to the police brigadier shooting case, when viewed from the percentage of each type of response.
Referensi
S. Mandasari, B. H. Hayadi, and R. Gunawan, “Analisis Sentimen Pengguna Transportasi Online Terhadap Layanan Grab Indonesia Menggunakan Multinomial Naive Bayes Classifier,” J. Teknol. Sist. Inf. dan Sist. Komput. TGD, vol. 5, no. 2, pp. 118–126, 2022.
R. N. Fahmi, Nursyifa, and A. Primajaya, “ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP KASUS PENEMBAKAN LASKAR FPI OLEH POLRI DENGAN METODE NAIVE BAYES CLASSIFIER,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 2, pp. 61–66, 2021, [Online]. Available: https://ejournal.akakom.ac.id/index.php/jiko/article/view/437.
M. I. Zul, F. Yulia, and D. Nurmalasari, “Social media sentiment analysis using K-means and naïve bayes algorithm,” Proc. - 2018 2nd Int. Conf. Electr. Eng. Informatics Towar. Most Effic. W. Mak. Deal. with Futur. Electr. Power Syst. Big Data Anal. ICon EEI 2018, no. February, pp. 24–29, 2018, doi: 10.1109/ICon-EEI.2018.8784326.
R. Rahmati and A. W. Wijayanto, “ANALISIS CLUSTER DENGAN ALGORITMA K-MEANS, FUZZY C-MEANS DAN HIERARCHICAL CLUSTERING,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 2, pp. 73–80, 2021.
G. F. Santoso et al., “Respon masyarakat terhadap kebijakan psbb sebagai penekan angka covid-19,” J. Inform. dan Komput., vol. 5, no. 2, pp. 38–48, 2021, [Online]. Available: https://ejournal.akakom.ac.id/index.php/jiko/article/view/401.
A. B. Saputra, P. W. Cahyo, M. Habibi, and A. Priadana, “Analysis and visualization of BPJS on twitter using K-means clustering,” Int. J. Heal. Sci. Technol., vol. 3, no. 3, pp. 109–117, 2022, [Online]. Available: https://ejournal.unisayogya.ac.id/index.php/ijhst/article/view/2466.
Y. W. Syaifudin and R. A. Irawan, “Implementasi Analisis Clustering Dan Sentimen Data Twitter Pada Opini Wisata Pantai Menggunakan Metode K-Means,” J. Inform. Polinema, vol. 4, no. 3, p. 189, 2018, doi: 10.33795/jip.v4i3.205.
P. Kherwa and P. Bansal, “Topic Modeling: A Comprehensive Review,” ICST Trans. Scalable Inf. Syst., vol. 7, no. 24, p. 159623, Jul. 2018, doi: 10.4108/eai.13-7-2018.159623.
V. S. Anoop and S. Asharaf, “Aspect-oriented sentiment analysis: A topic modeling-powered approach,” J. Intell. Syst., vol. 29, no. 1, pp. 1166–1178, 2020, doi: 10.1515/jisys-2018-0299.
N. L. P. M. Putu, Ahmad Zuli Amrullah, and Ismarmiaty, “Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 123–131, 2021, doi: 10.29207/resti.v5i1.2587.
T. D. Dikiyanti, A. M. Rukmi, and M. I. Irawan, “Sentiment analysis and topic modeling of BPJS Kesehatan based on twitter crawling data using Indonesian Sentiment Lexicon and Latent Dirichlet Allocation algorithm,” J. Phys. Conf. Ser., vol. 1821, no. 1, 2021, doi: 10.1088/1742-6596/1821/1/012054.
D. Ridhwanulah and D. H. Fudholi, “Pemodelan Topik pada Cuitan tentang Penyakit Tropis di Indonesia dengan Metode Latent Dirichlet Allocation,” J. Ilm. SINUS, vol. 20, no. 1, p. 11, 2022, doi: 10.30646/sinus.v20i1.589.
Hariyady, S. Basuki, and L. Meidina, “IMPLEMENTASI ALGORITMA DETEKSI TOPIK KELUHAN PELANGGAN JASA OJEK ONLINE BERDASARKAN KOMENTAR MEDIA SOSIAL,” Semin. Nas. Teknol. dan Rekayasa, vol. 5, pp. 160–166, 2019, [Online]. Available: http://research-report.umm.ac.id/index.php/sentra/article/view/3297.
R. Sanusi, F. D. Astuti, and I. Y. Buryadi, “Analisis sentimen pada twitter terhadap program kartu pra kerja dengan recurrent neural network,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 2, pp. 89–99, 2021.
S. Chohan, A. Nugroho, A. M. B. Aji, and W. Gata, “Analisis Sentimen Pengguna Aplikasi Duolingo Menggunakan Metode Naïve Bayes dan Synthetic Minority Over Sampling Technique,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 139–144, 2020, doi: 10.31294/p.v22i2.8251.
D. Haryalesmana and M. Wieriks, “ID-Stopwords,” 2019. [Online]. Available: https://github.com/masdevid/ID-Stopwords.
E. S. Negara and D. Triadi, “Topic modeling using latent dirichlet allocation (LDA) on twitter data with Indonesia keyword,” Bull. Soc. Informatics Theory Appl., vol. 5, no. 2, pp. 124–132, 2021, [Online]. Available: https://pubs.ascee.org/index.php/businta/article/view/455.
T. I. Saputra and R. Arianty, “IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING PADA ANALISIS SENTIMEN KELUHAN PENGGUNA INDOSAT,” J. Ilm. Inform. Komput., vol. 24, no. 3, pp. 191–198, Dec. 2019, doi: 10.35760/ik.2019.v24i3.2361.
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