PEMODELAN TOPIK TERKAIT BANJIR PADA TWITTER DENGAN MENGGUNAKAN LATENT DIRICHLET ALLOCATION
DOI:
https://doi.org/10.30989/teknomatika.v16i1.1139Keywords:
Banjir, LDA, Topic Modelling, Text Mining, Latent Dirichlet AllocationAbstract
In this background discusses the topic of tweet about Flooding on Twitter using the keyword "Flood". Tweet data was taken from June 1, 2021 to June 2, 2021 with the number of tweet data obtained, which was 2000 tweets. The number of tweets related to flooding has not been analyzed so that the topics contained in it are not yet known. Research . Modeling topics related to floods in Indonesia on Twitter social media with the LDA method. Research. This study uses experimental methods with several variables to test hypotheses. Then the data is processed with stages, namely web data extraction, preprocessing, feature extraction, topic modeling using latent dirichlet allocation algorithms, visualization, and analysis. Research. The results of the topic coherence stage were carried out a search for the most optimal topic from the 20 topics that had been determined at the beginning. The results of topic coherence for 20 topics concluded that for topic 10 it has a total topic value of 0.41 and has an ideal topic modeling result and is in accordance with the provisions. Conclusion : Based on the results of the discussion of topic coherence, it can be concluded that the most ideal number of topics is topic 10 because it has the highest value compared to other topics. The advice here is to be able to display or get flood information in Indonesia in real time and accurately.
References
[2] K. B. Putra dan R. P. Kusumawardani, “Analisis topik informasi publik media sosial di surabaya menggunakan pemodelan latent dirichlet allocation (LDA),” Jurnal Teknik ITS, vol. 6, no. 2, hlm. A446–A450, 2017.
[3] A. I. Alfanzar, K. Khalid, dan I. S. Rozas, “Topic modelling skripsi menggunakan metode latent diriclhet allocation,” JSiI (Jurnal Sistem Informasi), vol. 7, no. 1, hlm. 7–13, 2020.
[4] B. W. Arianto dan G. Anuraga, “Topic Modeling for Twitter Users Regarding the" Ruanggguru" Application,” Jurnal Ilmu Dasar, vol. 21, no. 2, hlm. 149–154, 2020.
[5] I. D. Susanti, R. D. Astuti, F. A. Sariasih, dan J. L. Putra, “PENGARUH BIAYA PROMOSI TERHADAP PENJUALAN PT. TEJA SEKAWAN JAKARTA UTARA,” Jurnal Mitra Manajemen, vol. 2, no. 4, hlm. 273–285, 2018.
[6] L. H. Pramono dan C. Subiyantoro, “Pengaruh Stemming Terhadap Ekstraksi Topik Menggunakan Metode Tf* Idf* Df Pada Aplikasi Pds,” JIKO (Jurnal Informatika dan Komputer), vol. 2, no. 1, 2017.
[7] Y. U. Al-khairi, Y. Wibisono, dan B. L. Putro, “Deteksi topik fashion pada twitter dengan latent dirichlet allocation,” 2017.
[8] R. Melita, V. Amrizal, H. B. Suseno, T. Dirjam, T. Informatika, dan F. Sains, “Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Syarah Umdatil Ahkam),” J. Tek. Inform, vol. 11, no. 2, hlm. 149–164, 2018.
[9] A. Rafiqi, “Penerapan Algoritma Fuzzy,” ADLN Univ. Airlangga,[Online]. Available: repository. unair. ac. id/29371/3/15 BAB II. pdf.
[10] K. E. Dewi, N. I. Widiastuti, dan E. Rainarli, “Evaluasi Sentence Extraction pada Peringkasan Dokumen Otomatis,” dipresentasikan pada SNIA (Seminar Nasional Informatika dan Aplikasinya), 2019, hlm. 8–12.
