Sentiment analysis on the increase of fuel prices on twitter (x) using naive bayes classifier.
DOI:
https://doi.org/10.37859/coscitech.v5i1.6954
Keywords:
Fuel Price Increase, Twitter, Naive Bayes Classifier Kenaikan BBM, Twitter, Naive Bayes Classifier
Abstract
In early September 2022, there was a shock from the news of the rise in fuel prices. The government decided to increase the price of fuel due to the surge in world oil prices. PT Pertamina (Persero) officially raised the price of Fuel Oil (BBM) one-third of September 2022, at 2:30 PM WIB (Western Indonesia Time). Since the decision, it has sparked opinions from the public. Many people expressed their responses through the social media platform Twitter, both in positive and negative ways. This resulted in both positive and negative sentiments from the public. The data used consisted of 3,000 tweets with the keyword "FUEL PRICE INCREASE," collected from November 1, 2022, to December 1, 2022. This research utilized the Naive Bayes Classifier method, conducted with three comparisons using thresholds ranging from 0.001 to 0.007. The experiment was conducted with three types of data testing: opinion data, mixed data (opinion-non-opinion), and balanced data. Here are the test results: for opinion data, the highest accuracy obtained was 80% with a ratio of 90:10, for mixed data, the accuracy obtained was 67.7% with a ratio of 70:30, and for balanced data, the accuracy obtained was 63.6% with a ratio of 90:10.
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References
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[3] M. W. A. Putra, Susanti, Erlin, and Herwin, “Analisis Sentimen Dompet Elektronik Pada Twitter Menggunakan Metode Naïve Bayes Classifier,” IT J. Res. Dev., vol. 5, no. 1, pp. 72–86, 2020, doi: 10.25299/itjrd.2020.vol5(1).5159.
[4] R. Nooraeni, A. B. Safiruddin, A. F. Afifah, K. D. Agung, and N. N. Rosyad, “Analisis Sentimen Publik terhadap Sistem Zonasi Sekolah Menggunakan Data Twitter dengan Metode Naïve Bayes Classification,” Fakt. Exacta, vol. 12, no. 4, p. 315, 2020, doi: 10.30998/faktorexacta .v12i4.5205.
[5] S. Suryono and E. Taufiq Luthfi, “Analisis sentimen pada Twitter dengan menggunakan metode Naïve Bayes Classifier,” Jnanaloka, pp. 81–86, 2021, doi: 10.36802/jnanaloka.2020.v1-no2-81-86.
[6] B. M. Pintoko and K. M. L., “Analisis Sentimen Jasa Transportasi Online pada Twitter Menggunakan Metode Naive Bayes Classifier,” e-Proceeding Eng., vol. 5, no. 3, pp. 8121–8130, 2018.
[7] A. Pramono, R. Indriati, and A. Nugroho, “Sentiment Analysis Tokoh Politik Pada Twitter,” Semin. Nas. Inov. Teknol. , pp. 195–200, 2017, [Online]. Available: https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/403/317
[8] Z. Saharuna, P. Negeri, U. Pandang, M. Nur, and Y. Utomo, “Analisis Sentimen pada Twitter terhadap Pelayanan Pemerintah Kota Makassar Eddy Tungadi Politeknik Negeri Ujung Pandang,” Proceeding SNTEI, pp. 87–93, 2019, [Online]. Available: https://dev.Twitter.com
[9] R. Apriani et al., “Analisis Sentimen dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia,” J. Rekayasa Teknol. Nusa Putra, vol. 6, no. 1, pp. 54–62, 2019, [Online]. Available: https://rekayasa.nusaputra.ac.id/article/view/86
[10] M. K. Anam, B. N. Pikir, and M. B. Firdaus, “Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 139–150, 2021, doi: 10.30812/matrik.v21i1.1092.
[11] F. S. Pattiiha and H. Hendry, “Perbandingan Metode K-NN, Naïve Bayes, Decision Tree untuk Analisis Sentimen Tweet Twitter Terkait Opini Terhadap PT PAL Indonesia,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 2, p. 506, 2022, doi: 10.30865/jurikom.v9i2.4016.
[12] M. I. Fikri, T. S. Sabrila, Y. Azhar, and U. M. Malang, “Comparison of the Naïve Bayes Method and Support Vector Machine on Twitter Sentiment Analysis,” SMATIKA J. STIKI Inform. J., vol. 10, no. 2, pp. 71–76, 2020.
[13] R. D. Septiana, A. B. Susanto, and T. Tukiyat, “Analisis Sentimen Vaksinasi Covid-19 Pada Twitter Menggunakan Naive Bayes Classifier Dengan Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization,” J. SISKOM-KB (Sistem Komput. dan Kecerdasan Buatan), vol. 5, no. 1, pp. 49–56, 2021, doi: 10.47970/siskom-kb.v5i1.228.
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[15] D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” J. TEKNO KOMPAK, vol. 15, no. 1, pp. 131–145, 2020.
[16] M. Syarifuddinn, “Analisis Sentimen Opini Publik Terhadap Efek Psbb Pada Twitter Dengan Algoritma Decision Tree,Knn, Dan Naïve Bayes,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 87–94, 2020, doi: 10.33480/inti.v15i1.1433.
[17] K. V. S. Toy, Y. A. Sari, and I. Cholissodin, “Analisis Sentimen Twitter menggunakan Metode Naive Bayes dengan Relevance Frequency Feature Selection (Studi Kasus: Opini Masyarakat mengenai Kebijakan New Normal),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 11, pp. 5068–5074, 2021, [Online]. Available: http://j-ptiik.ub.ac.id
[18] R. A. Raharjo, I. M. G. Sunarya, and D. G. H. Divayana, “Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Kasus Analisis Sentimen Terhadap Data Vaksin Covid-19 Di Twitter,” Elkom J. Elektron. dan Komput., vol. 15, no. 2, pp. 456–464, 2022, doi: 10.51903/elkom.v15i2.918.
[19] R. N. Fahmi, N. 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/437/0
[20] D. I. Sari, Y. F. Wati, and Widiastuti, “Analisis Sentimen Dan Klasifikasi Tweets Berbahasa Indonesia Terhadap Transportasi Umum Mrt Jakarta Menggunakan Naïve Bayes Classifier,” J. Ilm. Inform. Komput., vol. 25, no. 1, pp. 64–75, 2020, doi: 10.35760/ik.2020.v25i1.2427.
[21] D. A. Wulandari, R. Rohmat Saedudin, and R. Andreswari, “Analisis Sentimen Media Sosial Twitter Terhadap Reaksi Masyarakat Pada Ruu Cipta Kerja Menggunakan Metode Klasifikasi Algoritma Naive Bayes Analysis of Twitter Social Media Sentiment on the Public’S Reaction To the Drafts of Job Creation Law Using the Cla,” e-Proceeding Eng., vol. 8, no. 5, pp. 9007–9016, 2021.
[22] F.Wulandari, E. Haerani, M. Fikry, and E. Budianita, “Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no.1, pp. 88-96, 2023, doi: 10.37859/coscitech.v4i1.4781.
[23] F. A. Wenando, R. Hayami, Soni, A. Fitria, and D. F. Shifana, “Sentimen analisis masyarakat terhadap kasus penembakan brigadir j menggunakan algoritma naive bayes classifier,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no.2, pp. 484-490, 2023, doi: 10.37859/coscitech.v4i2.5686.
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Published
2024-05-01
How to Cite
Muhammad Abdillah, Fikry, M., Yusra, Nazir, A., & Insani, F. (2024). Sentiment analysis on the increase of fuel prices on twitter (x) using naive bayes classifier. Jurnal CoSciTech (Computer Science and Information Technology), 5(1), 65–74. https://doi.org/10.37859/coscitech.v5i1.6954
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