Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina
DOI:
https://doi.org/10.62951/bridge.v2i3.180Keywords:
classification, sentiment analysis, support vector machine, text miningAbstract
The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.
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Abdillah, G., Putra, F. A., Renaldi, F., & ... (2016). Penerapan data mining pemakaian air pelanggan untuk menentukan klasifikasi potensi pemakaian air pelanggan baru di PDAM Tirta Raharja. Seminar Nasional Informatika, 2016(Sentika), 18–19. https://fti.uajy.ac.id/sentika/publikasi/makalah/2016/43.pdf
Aizawa, A. (2003). An information-theoretic perspective of TF-IDF measures. Information Retrieval, 39, 45–65.
Alhaq, Z., Mustopa, A., Mulyatun, S., & Santoso, J. D. (2021). Penerapan metode support vector machine untuk analisis sentimen pengguna Twitter. Journal of Information System Management (JOISM), 3(2), 44–49. https://doi.org/10.24076/joism.2021v3i2.558
Anonim. (2022). Kamus besar bahasa Indonesia. https://kbbi.kemdikbud.go.id/, 9 November 2022
Azhar, Y. (2018). Metode lexicon-learning based untuk identifikasi tweet opini berbahasa Indonesia. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 6(3), 237. https://doi.org/10.23887/janapati.v6i3.11739
Budi Santosa, B. (2007). Data mining: Teknik pemanfaatan data untuk keperluan bisnis. Garah Ilmu.
Buntoro, G. A. (2017). Analisis sentimen calon gubernur DKI Jakarta 2017 di Twitter. Jurnal Informatika, 2(1), 32–41.
Chandani, V., & Wahono, R. S. (2015). Komparasi algoritma klasifikasi machine learning dan feature selection pada analisis sentimen review film. Journal of Intelligent Systems, 1(1), 55–59.
Das, S., & Nene, M. J. (2017). A survey on types of machine learning techniques in intrusion prevention systems. In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 2296–2299). https://doi.org/10.1109/WiSPNET.2017.8300169
Feldman, R., & Sanger, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press.
Fitriyah, N., Warsito, B., & Maruddani, D. A. I. (2020). Analisis sentimen Gojek pada media sosial Twitter dengan klasifikasi support vector machine (SVM). Jurnal Gaussian, 9(3), 376–390. https://doi.org/10.14710/j.gauss.v9i3.28932
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.
Gunadi, G., & Sensuse, D. I. (2012). Penerapan metode data mining market basket analysis terhadap data penjualan produk buku dengan menggunakan algoritma apriori dan frequent pattern growth (FP-Growth). Telematika, 4(1), 118–132.
Koto, F., & Rahmaningtyas, G. Y. (2018). Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs. In Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017 (pp. 391–394). https://doi.org/10.1109/IALP.2017.8300625
Lesmeister, C. (2015). Mastering machine learning with R: Master machine learning techniques with R to deliver insights for complex projects. Packt Publishing.
Mahbubah, L. D., & Zuliarso, E. (2019). Analisa sentimen Twitter pada pilpres 2019 menggunakan algoritma Naive Bayes. Sintak, 194–195. https://www.unisbank.ac.id/ojs/index.php/sintak/article/view/7585
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
Mooney, R., & Wong, Y. W. (2006). Learning for semantic parsing with statistical machine translation. In Proceedings of The Human Language Technology Conference of the NAACL (pp. 439–446).
Muhammadi, R. H., Laksana, T. G., & Arifa, A. B. (2022). Combination of support vector machine and lexicon-based algorithm in Twitter sentiment analysis. Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika, 8(1), 59–71. https://doi.org/10.23917/khif.v8i1.15213
Nandini, R. A., Sari, Y. A., & Adikara, P. P. (2019). Analisis sentimen impor beras 2018 pada Twitter menggunakan metode support vector machine dan pembobotan jumlah retweet. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(4), 3396–3406.
Pisner, D. A., & Schnyer, D. M. (2019). Support vector machine. In Machine Learning: Methods and Applications to Brain Disorders (pp.
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