Implementasi LDA untuk Pengelompokan Topik Twitter Bertagar #Mypertamina
Keywords:
MyPertamina, Twitter, LDA
Abstract
Abstract
Twitter social media is widely used by users as a medium of communication and information. Apart from being a communication tool, Twitter is used to obtain the required research data. The use of the twitter hashtag becomes a reference for trending news or issues that are developing in the community. The trend that is currently being discussed is the Mypertamina application. This study takes data from twitter with the hashtag #Mypertamina with a lot of twitter data as many as 149 tweets, from the data obtained it will be clustered using topic modeling with the Latent Dirichlet Allocation (LDA) method. The advantage of the LDA method is that it can cluster, summarize, and link large amounts of data. This study resulted in 3 data clusters with the largest coherence value of 0.4618
Keywords: 3-5 keywords; Mypertamina, Twitter, LDA
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Carneiro, A., Matos, M. J., Uriarte, E., & Santana, L. (2021). Trending topics on coumarin and its derivatives in 2020. In Molecules (Vol. 26, Issue 2). MDPI AG. https://doi.org/10.3390/molecules26020501
Hasan, M., Rahman, A., Karim, M. R., Khan, M. S. I., & Islam, M. J. (2021). Normalized approach to find optimal number of topics in latent dirichlet allocation (lda). Advances in Intelligent Systems and Computing, 1309, 341–354. https://doi.org/10.1007/978-981-33-4673-4_27
Ibrahim, R. M., & Karina Moeliono, N. N. (2020). Persepsi manfaat, kepercayaan, efikasi diri, kemudahan penggunaan, keamanan terhadap persepsi konsumen pada my pertamina (Studi pada penggunaan my pertamina kota Bandung. Jurnal Ilmiah Mahasiswa Ekonomi Manajemen Accredited SINTA, 4(2), 396–413.
Impraimakis, M., & Smyth, A. W. (2022). Input–parameter–state estimation of limited information windâ€excited systems using a sequential Kalman filter. Structural Control and Health Monitoring, 29(4). https://doi.org/10.1002/stc.2919
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. In Multimedia Tools and Applications (Vol. 78, Issue 11). https://doi.org/10.1007/s11042-018-6894-4
Kearney, M. (2019). rtweet: Collecting and analyzing Twitter data. Journal of Open Source Software, 4(42), 1829. https://doi.org/10.21105/joss.01829
Purwitasari, D., Aida Muflichah, Novrindah Alvi Hasanah, & Agus Zainal Arifin. (2021). Pemodelan Topik dengan LDA untuk Temu Kembali Informasi dalam Rekomendasi Tugas Akhir. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 421–428. https://doi.org/10.29207/resti.v5i3.3049
Putri, S. A., Kusuma, P. D., & Setianingsih, C. (2021). Clustering Topik Pada Data Sentimen BPJS Kesehatan Menggunakan Metode Laten Dirichlet Allocation. E-Proceeding of Engineering, 8(5), 6097–6105.
Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020). Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. 101–108. https://doi.org/10.18653/v1/2020.acl-demos.14
Rashif, F., Ihza Perwira Nirvana, G., Alif Noor, M., & Aini Rakhmawati, N. (2021). Implementasi LDA untuk Pengelompokan Topik Cuitan Akun Bot Twitter bertagar #Covid-19 LDA Implementation for Topic of Bot’s Tweets with #Covid-19 Hashtag. Cogito Smart Journal |, 7(1), 170–181.
Sezer, O. B., & Ozbayoglu, A. M. (2020). Financial trading model with stock bar chart image time series with deep convolutional neural networks. Intelligent Automation and Soft Computing, 26(2), 323–334. https://doi.org/10.31209/2018.100000065
Sistem, R., Sahria, Y., & Fudholi, D. H. (2021). JURNAL RESTI Analisis Topik Penelitian Kesehatan di Indonesia Menggunakan Metode. Jurnal Rekayasa Sistem Dan Teknologi Informasi (RESTI), 1(10), 336–344.
Sohail, S. S., Khan, M. M., Arsalan, M., Khan, A., Siddiqui, J., Hasan, S. H., & Alam, M. A. (2021). Crawling Twitter data through API: A technical/legal perspective.
Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Data processing and text mining technologies on electronic medical records: A review. In Journal of Healthcare Engineering (Vol. 2018). Hindawi Limited. https://doi.org/10.1155/2018/4302425
Yu, P., Xie, S., Ma, X., Jia, B., Pang, B., Gao, R., Zhu, Y., Zhu, S.-C., & Wu, Y. N. (2022). Latent Diffusion Energy-Based Model for Interpretable Text Modeling. 2020.
Published
2023-02-28
Section
Articles
Jurnal Ekonomi dan Teknik Informatika
