Analisis Penentuan Respons Twitter sebagai Media Komunikasi dan Informasi Pemerintah Berbasis Metode Rabin-Karp
Abstract
Social media in this digital era, especially Twitter, has an important role in the interaction between government and society. Given the wide variety of activities within government circles, the task of monitoring and responding to messages is complex and time-consuming. Therefore, this study aims to develop an effective approach in determining the appropriate response from the government to people's tweets. This study proposes using a combination of the Rabin-Karp method to quickly determine relevant responses. The Rabin-Karp method, known for its efficiency in pattern matching, is used to match tweets to a set of tweets that have already been given a response. Furthermore, the Word2Vec technique is used to improve understanding of the meaning of the text. The use of the Rabin-Karp method with the addition of the Word2Vec method shows that the response accuracy rate is 74.55%. The results of this study also show that the lower the K-Gram value, the higher the similarity value, and vice versa. These results are expected to contribute in the context of government that is responsive to societal issues discussed on Twitter.
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References
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[2] P. A. Permatasari, L. Linawati, and L. Jasa, “Survei Tentang Analisis Sentimen Pada Media Sosial,” Maj. Ilm. Teknol. Elektro, vol. 20, no. 2, 2021, doi: 10.24843/mite.2021.v20i02.p01.
[3] R. H. Dwiwina and K. Y. S. Putri, “The Use of the Auto Base Accounts on Twitter as A Media for Sharing Opinions,” Ultim. J. Ilmu Komun., 2021, doi: 10.31937/ultimacomm.v13i1.1603.
[4] A. A. Pailendra and T. Gunarto, “Analisis Pengaruh Peran Masyarakat dan Pemerintah terhadap Pariwisata Curug Klawas Di Lampung Utara,” E-journal F. Econ. Bus. Entrep., vol. 1, no. 3, pp. 237–244, Oct. 2022, doi: 10.23960/efebe.v1i3.40.
[5] Y. Astuti and I. R. Wulandari, “An arrangement of the number of K-grams in the performance of Rabin Karp algorithm in text adjustment,” Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 3, 2022, doi: 10.11591/ijeecs.v26.i3.pp1388-1394.
[6] F. Husain and O. Uzuner, “Investigating the Effect of Preprocessing Arabic Text on Offensive Language and Hate Speech Detection,” ACM Trans. Asian Low-Resource Lang. Inf. Process., vol. 21, no. 4, 2022, doi: 10.1145/3501398.
[7] V. A. Flores, L. Jasa, and L. Linawati, “Analisis Sentimen untuk Mengetahui Kelemahan dan Kelebihan Pesaing Bisnis Rumah Makan Berdasarkan Komentar Positif dan Negatif di Instagram,” Maj. Ilm. Teknol. Elektro, vol. 19, no. 1, 2020, doi: 10.24843/mite.2020.v19i01.p07.
[8] S. Juniarsih, E. F. Ripanti, and E. E. Pratama, “Implementasi Naive Bayes Classifier pada Opinion Mining Berdasarkan Tweets Masyarakat Terkait Kinerja Presiden dalam Aspek Ekonomi,” J. Sist. dan Teknol. Inf., vol. 8, no. 3, 2020, doi: 10.26418/justin.v8i3.39118.
[9] S. Styawati, A. Nurkholis, A. A. Aldino, S. Samsugi, E. Suryati, and R. P. Cahyono, “Sentiment Analysis on Online Transportation Reviews Using Word2Vec Text Embedding Model Feature Extraction and Support Vector Machine (SVM) Algorithm,” 2022, doi: 10.1109/ISMODE53584.2022.9742906.
[10] R. Kosasih and A. Alberto, “Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier,” Ilk. J. Ilm., vol. 13, no. 2, 2021, doi: 10.33096/ilkom.v13i2.721.101-109.
[11] B. R. Lidiawaty, M. E. Zulfaqor, O. Diyantara, and D. R. S. Dewi, “Keywords Generator From Paragraph Text Using Text Mining in Bahasa Indonesia,” 2022, doi: 10.1109/IRTM54583.2022.9791753.
[12] I. D. Wahyono et al., “Shared Nearest Neighbour in Text Mining for Classification Material in Online Learning Using Mobile Application,” Int. J. Interact. Mob. Technol., vol. 16, no. 4, 2022, doi: 10.3991/ijim.v16i04.28991.
[13] S. K. Bharti, R. K. Gupta, S. Patel, and M. Shah, “Context-Based Bigram Model for POS Tagging in Hindi: A Heuristic Approach,” Ann. Data Sci., 2022, doi: 10.1007/s40745-022-00434-4.
[14] Y. Qiao, W. Zhang, X. Du, and M. Guizani, “Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security,” ACM Trans. Internet Technol., vol. 22, no. 1, 2022, doi: 10.1145/3436751.
[15] S. Wang, C. Aggarwal, and H. Liu, “Beyond word2vec: Distance-graph tensor factorization for word and document embeddings,” 2019, doi: 10.1145/3357384.3358051.
[16] K. A. Sekarwati, L. Y. Banowosari, I. M. Wiryana, and D. Kerami, “Pengukuran Kemiripan Dokumen dengan Menggunakan Tools Gensim,” Pros. SNST ke-6 Tahun 2015, 2015.
[17] I. Z. Simanjuntak, “Analisa Kombinasi Algoritma Stemming Dan Algoritma Soundex Dalam Pencarian Kata Bahasa Indonesia,” Inf. dan Teknol. Ilm., vol. 10, no. 1, 2022.
[18] S. L. B. Ginting, Y. R. Ginting, S. Sutono, and W. A. Sirait, “Aplikasi Deteksi Kemiripan Kata Menggunakan Algoritma Rabin-Karp,” J. Teknol. dan Inf., vol. 12, no. 2, 2022, doi: 10.34010/jati.v12i2.6947.
[19] A. C. D. Sitepu, “Representasi Teknik Information Retrieval Pada Perhitungan Rabin-Karp Menggunakan Stemming Nazief-Adriani,” JUTISAL J. Tek. Inform. Univers., vol. 2, no. 1, 2022.
[20] E. Hamir, D., & Ekohariadi, “MENGGUNAKAN ALGORITMA RABIN KARP UNTUK MATA PELAJARAN INFORMATIKA DI MA KANJENG SEPUH Dimas Tifli Irhami Hamir Ekohariadi Abstrak,” J. Inf. Technol. Educ., pp. 19–27, 2018.
[21] T. T. I. Billhaqqi, G. W. Wicaksono, and C. S. K. Aditya, “Comparison analysis of Rabin-Karp and Winnowing algorithms in automated essay answer assessment system,” in AIP Conference Proceedings, 2022, vol. 2453, doi: 10.1063/5.0095186.
[22] I. N. S. W. Wijaya, K. A. Seputra, and W. G. S. Parwita, “Comparison of the BM25 and rabinkarp algorithm for plagiarism detection,” in Journal of Physics: Conference Series, 2021, vol. 1810, no. 1, doi: 10.1088/1742-6596/1810/1/012032.
Published
2024-03-03
How to Cite
SARI, Luh Ayu Diah Fernita; SASTRA, Nyoman Putra Putra; HARTATI, Rukmi Sari.
Analisis Penentuan Respons Twitter sebagai Media Komunikasi dan Informasi Pemerintah Berbasis Metode Rabin-Karp.
Majalah Ilmiah Teknologi Elektro, [S.l.], v. 22, n. 2, p. 265-272, mar. 2024.
ISSN 2503-2372.
Available at: <https://ojs.unud.ac.id/index.php/mite/article/view/104220>. Date accessed: 19 nov. 2024.
doi: https://doi.org/10.24843/MITE.2023.v22i02.P15.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
