Klasifikasi Lexicon-Based Sentiment Analysis Tragedi Kanjuruhan pada Twitter Menggunakan Algoritma Convolutional Neural Network
DOI:
https://doi.org/10.55606/juisik.v4i1.759Keywords:
Twitter, Kanjuruhan Disaster, Sentiment Analysis, Lexicon, Convolutional Neural NetworkAbstract
This study aims to conduct a sentiment analysis of conversations on social media Twitter related to the Kanjuruhan Tragedy. Social media, especially Twitter, has become a significant platform for Indonesians to share their thoughts and feelings regarding this tragic event. We used two approaches for sentiment analysis, namely Lexicon-based and Convolutional Neural Network (CNN), with a focus on classifying sentiments in positive, negative, and neutral categories. This study also involves references to several previous studies that implemented various sentiment analysis methods. It is hoped that the results of this study can provide deep insight into the responses and feelings of the public on social media related to the Kanjuruhan Tragedy. The lexicon-based sentiment analysis classification of the Kanjuruhan Tragedy on twitter social media using the CNN algorithm successfully analyzed the sentiment results of tweets related to the tragedy where most of the tweets obtained had negative sentiments with test results of precision value 87.74%, recall 87.51%, and f1-score 87.27% with a classification accuracy of 87.27% and took 3 minutes 23 seconds of training time.
References
Ahmad, M., Ferdy Octaviansyah, M., Kardiana, A., & Fadli Prasetyo, K. (2019). Sentiment Analysis System of Indonesian tweets using lexicon and naïve Bayes approach. 2019 Fourth International Conference on Informatics and Computing (ICIC). https://doi.org/10.1109/icic47613.2019.8985930
Anggina, S., Setiawan, N. Y., & Bachtiar, F. A. (2022). Analisis Ulasan Pelanggan Menggunakan multinomial naïve Bayes classifier dengan lexicon-based Dan tf-IDF pada formaggio coffee and resto. Is The Best Accounting Information Systems and Information Technology Business Enterprise This Is Link for OJS Us, 7(1), 76–90. https://doi.org/10.34010/aisthebest.v7i1.7072
Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment analysis based on Deep Learning: A Comparative Study. Electronics, 9(3), 483. https://doi.org/10.3390/electronics9030483
Hendriyanto, M. D., Ridha, A. A., & Enri, U. (2022). Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store menggunakan algoritma support vector machine. INTECOMS: Journal of Information Technology and Computer Science, 5(1), 1–7. https://doi.org/10.31539/intecoms.v5i1.3708
Humas Kemenko Polhukam RI. (2022, November 25). Laporan TGIPF Tragedi Kanjuruhan. Kemenko Polhukam R.I. Retrieved March 3, 2023, from https://polkam.go.id/laporan-tgipf-tragedi-kanjuruhan/
Jayani, D. H. (2020). 10 media Sosial Yang Paling Sering Digunakan di Indonesia: Databoks. Pusat Data Ekonomi dan Bisnis Indonesia. Retrieved March 3, 2023, from https://databoks.katadata.co.id/datapublish/2020/02/26/10-media-sosial-yang-paling-sering-digunakan-di-indonesia
Krisdiyanto, T. (2021). Analisis Sentimen Opini Masyarakat Indonesia Terhadap Kebijakan PPKM Pada media sosial Twitter menggunakan naïve Bayes clasifiers. Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer Dan Teknologi Informasi, 7(1), 32. https://doi.org/10.24014/coreit.v7i1.12945
Munasatya, N., & Novianto, S. (2020). Natural language processing Untuk Sentimen Analisis presiden jokowi menggunakan multi layer perceptron. Techno.Com, 19(3), 237–244. https://doi.org/10.33633/tc.v19i3.3630
Pratiwi, S. Y., & Nudin, S. R. (2021). Analisis Sentimen TERHADAP facebook marketplace Menggunakan METODE lexicon based Dan Support Vector Machine. Jurnal Ilmiah Teknologi Informasi Dan Robotika, 3(2), 9–15. https://doi.org/10.33005/jifti.v3i2.55
Rozi, I. F., Pramono, S. H., & Dahlan, E. A. (2012). Implementasi Opinion Mining (Analisis Sentimen) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi. Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), 6(1), 37–43. https://doi.org/https://doi.org/10.21776/jeeccis.v6i1.164
Sari, A. P., Suzuki, H., Kitajima, T., Yasuno, T., Prasetya, D. A., & Nachrowie, N. (2020). Prediction model of wind speed and direction using convolutional neural network - long short term memory. 2020 IEEE International Conference on Power and Energy (PECon), 356–361. https://doi.org/10.1109/pecon48942.2020.9314474
Sihananto, A. N., Al Haromainy, M. M., Sari, A. P., Mubarrok, A. H., & Ramadhan, D. (2022). PEMILAHAN JENIS SAMPAH MENGGUNAKAN ALGORITMA CNN. SCAN - Jurnal Teknologi Informasi Dan Komunikasi, XVII(3), 23–27.
Singh, C., Imam, T., Wibowo, S., & Grandhi, S. (2022). A deep learning approach for sentiment analysis of COVID-19 reviews. Applied Sciences, 12(8), 3709. https://doi.org/10.3390/app12083709
Wiradana, B. C. (2023). Perbandingan Akurasi Algoritma Multinomial, Bernoulli Dan Gaussian Naïve Bayes Untuk Sentimen Analisis Hacker Bjorka Pada Sosial Media Twitter. UNIVERSITAS PEMBANGUNAN NASIONAL “VETERAN” JAWA TIMUR.
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