Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Kasus Analisis Sentimen Terhadap Data Vaksin Covid-19 Di Twitter
DOI:
https://doi.org/10.51903/elkom.v15i2.918Keywords:
Perbandingan Metode Klasifikasi, Analisis Sentimen, Vaksin Covid-19, Twitter, Naïve Bayes Classifier, Support Vector MachineAbstract
Organisasi Kesehatan Dunia (WHO) secara resmi menyebut virus Covid-19 sebagai pandemi global, oleh karena itu semua negara di dunia berusaha meminimalkan dampak yang ditimbulkan oleh virus tersebut, yaitu dengan mengembangkan vaksin. Dalam konteks pandemi Covid-19, pemerintah Indonesia juga meminta dan mendorong masyarakat untuk turut serta mendukung vaksinasi, namun upaya tersebut sebenarnya memiliki kelebihan dan kekurangan, sehingga banyak masyarakat yang mengutarakan pendapatnya di jejaring sosial salah satunya Twitter. Penelitian ini bertujuan untuk mengetahui hasil penerapan analisis sentimen dan mengukur performansi algoritma Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM) terhadap data vaksin Covid-19 dengan cara mengklasifikasikan data tersebut ke dalam kelas positif dan negatif. Data tweet yang didapat kemudian dilakukan text preprocessing untuk mengoptimalkan pengolahan data. Terdapat 4 tahapan text preprocessing antara lain Case Folding, Tokenizing, Filtering, dan Stemming. Penelitian ini mengkaji kinerja Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM) dengan menambahkan teknik TF-IDF (Term Frequency-Inverse Document Frequency) yang bertujuan untuk memberikan bobot pada hubungan kata (term) sebuah dokumen. Kemudian melakukan splitting data yaitu membagi data training 80% dan data testing 20% dengan harapan mendapatkan model dengan performansi terbaik dan yang terakhir melakukan visualisasi data tweet dengan menggunakan Word Cloud agar bisa menarik sebuah kesimpulan. Hasil klasifikasi data tweet vaksin Covid-19 menggunakan algoritma Naïve Bayes Classifier mendapatkan nilai accuracy sebesar 81%, precision sebesar 80%, recall sebesar 99%, dan f1-score sebesar 89%, Sedangkan untuk algoritma Support Vector Machine mendapatkan nilai accuracy sebesar 87%, precision sebesar 88%, recall sebesar 96%, dan f1-score sebesar 92%.References
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[2] Anees, A. F., Shaikh, A., Shaikh, A., & Shaikh, S. (2020). Survey Paper on Sentiment Analysis : Techniques and Challenges. EasyChair, 2516–2314.
[3] Astari, N. M. A. J., Dewa Gede Hendra Divayana, & Gede Indrawan. (2020). Analisis Sentimen Dokumen Twitter Mengenai Dampak Virus Corona Menggunakan Metode Naive Bayes Classifier. Jurnal Sistem Dan Informatika (JSI), 15(1), 27–29. https://doi.org/10.30864/jsi.v15i1.332
[4] D’Andrea, A., Ferri, F., Grifoni, P., & Guzzo, T. (2015). Approaches, Tools and Applications for Sentiment Analysis Implementation. International Journal of Computer Applications, 125(3), 26–33. https://doi.org/10.5120/ijca2015905866
[5] Dey, S., Wasif, S., Tonmoy, D. S., Sultana, S., Sarkar, J., & Dey, M. (2020). A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews. 2020 International Conference on Contemporary Computing and Applications, IC3A 2020, February, 217–220. https://doi.org/10.1109/IC3A48958.2020.233300
[6] Fridom Mailo, F., Lazuardi, L. (2019). Analisis Sentimen Data Twitter Menggunakan Metode Text Mining Tentang Masalah Obesitas di Indonesia. Jurnal Sistem Informasi Kesehatan Masyarakat Journal of Information Systems for Public Health, 4(1), 28–36. https://jurnal.ugm.ac.id/jisph/article/view/44455
[7] Giovani, A. P., Ardiansyah, A., Haryanti, T., Kurniawati, L., & Gata, W. (2020). Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi. Jurnal Teknoinfo, 14(2), 115. https://doi.org/10.33365/jti.v14i2.679
[8] Gokgoz, E., & Subasi, A. (2015). Biomedical Signal Processing and Control Comparison of decision tree algorithms for EMG signal classification using DWT. Biomedical Signal Processing and Control, 18, 138–144.
Ilmiah, J. P. (2017). Text Mining Dan Sentimen Analisis Twitter Pada Gerakan Lgbt. Intuisi : Jurnal Psikologi Ilmiah, 9(1), 18–25. https://doi.org/10.15294/intuisi.v9i1.9561
[9] Irsyad, H., Farisi, A., & Pribadi, M. R. (2019). Klasifikasi Opini Masyarakat Terhadap Jasa ISP MyRepublic dengan Naïve Bayes. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI), 8(1), 30. https://doi.org/10.22146/jnteti.v8i1.487
[10] KOMINFO, KEMENKES, K. P. (2021). Data Vaksinasi COVID-19 (Update per 9 Oktober 2021). https://covid19.go.id/berita/data-vaksinasi-covid-19-update-9-oktober-2021
[11] Lestandy, M., Abdurrahim, A., & Syafa’ah, L. (2021). Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(10), 802–808.
[12] Lin, Y., Wang, X., & Zhou, A. (2016). Opinion spam detection. Opinion Analysis for Online Reviews, May, 79–94. https://doi.org/10.1142/9789813100459_0007
[13] Liu, C., Zhou, Q., Li, Y., Garner, L. V., Watkins, S. P., Carter, L. J., Smoot, J., Gregg, A. C., Daniels, A. D., Jervey, S., & Albaiu, D. (2020). Research and Development on Therapeutic Agents and Vaccines for COVID-19 and Related Human Coronavirus Diseases. ACS Central Science, 6(3), 315–331. https://doi.org/10.1021/acscentsci.0c00272
[14] Mean, R., Error, S., Mean, R., Error, S., Matrix, C., Curve, R. O. C., Curve, A. U., Evaluation, I., & Evaluation, E. (n.d.). Evaluasi dan Validasi Evaluasi.
[15] Mika Parwita, I. M., & Siahaan, D. (2019). Classification of Mobile Application Reviews using Word Embedding and Convolutional Neural Network. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 10(1), 1. https://doi.org/10.24843/lkjiti.2019.v10.i01.p01
[16] Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., & Stoyanov, V. (2016). SemEval-2016 task 4: Sentiment analysis in twitter. SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings, 1–18. https://doi.org/10.18653/v1/s16-1001
[17] Prastyo, P. H., Sumi, A. S., Dian, A. W., & Permanasari, A. E. (2020). Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel. Journal of Information Systems Engineering and Business Intelligence, 6(2), 112. https://doi.org/10.20473/jisebi.6.2.112-122
[18] President of the Republic of Indonesia. (2020). Presidential Regulation 99/2020 on COVID-19 Vaccine Procurement [Peraturan Presiden No. 99 Tahun 2020 tentang Pengadaan Vaksin dan Pelaksanaan Vaksinasi dalam Rangka Penanggulangan Pandemi Corona Virus Disease 2019 (Covid-19)]. Presidential Regulation, 2019(039471), 1–13. https://peraturan.bpk.go.id/Home/Details/147944/perpres-no-99-tahun-2020
[19] Rachman, F. F., & Pramana, S. (2020). Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter. Health Information Management Journal, 8(2), 100–109. https://inohim.esaunggul.ac.id/index.php/INO/article/view/223/175
[20] Rachman, F., Health, S. P.-I. of, & 2020, undefined. (2020). Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter. Inohim.Esaunggul.Ac.Id, 8(2), 2655–9129. https://inohim.esaunggul.ac.id/index.php/INO/article/download/223/175
[21] Rahmadya Trias Handayanto, H. (2020). Data Mining dan Machine Learning Menggunakan Matlab dan Python (Oktober 20). Informatika Bandung. https://openlibrary.telkomuniversity.ac.id/pustaka/163039/data-mining-dan-machine-learning-menggunakan-matlab-dan-python.html
[22] Rasool, A., Tao, R., Marjan, K., & Naveed, T. (2019). Twitter Sentiment Analysis: A Case Study for Apparel Brands. Journal of Physics: Conference Series, 1176(2). https://doi.org/10.1088/1742-6596/1176/2/022015
[23] Rochmawati, N., & Wibawa, S. C. (2018). Opinion Analysis on Rohingya using Twitter Data. IOP Conference Series: Materials Science and Engineering, 336(1). https://doi.org/10.1088/1757-899X/336/1/012013
Sistem, R., Lestandy, M., Abdurrahim, A., & Syafa, L. (2021). Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent. 5(10), 802–808.
[24] Song, J., Kim, K. T., Lee, B., Kim, S., & Youn, H. Y. (2017). A novel classification approach based on Naïve Bayes for Twitter sentiment analysis. KSII Transactions on Internet and Information Systems, 11(6), 2996–3011. https://doi.org/10.3837/tiis.2017.06.011
[25] Susilo, A., Rumende, C. M., Pitoyo, C. W., Santoso, W. D., Yulianti, M., Herikurniawan, H., Sinto, R., Singh, G., Nainggolan, L., Nelwan, E. J., Chen, L. K., Widhani, A., Wijaya, E., Wicaksana, B., Maksum, M., Annisa, F., Jasirwan, C. O. M., & Yunihastuti, E. (2020). Coronavirus Disease 2019: Tinjauan Literatur Terkini. Jurnal Penyakit Dalam Indonesia, 7(1), 45. https://doi.org/10.7454/jpdi.v7i1.415
[26] Syarifuddin, M. (2020). Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn. Inti Nusa Mandiri, 15(1), 23–28.
[27] Tri, F., & Saputra, G. (2020). Analisis Sentimen Bahasa Indonesia Berbasis Leksikon pada Twitter Pembahasan Pustaka.
[28] Watrianthos, R., Suryadi, S., Irmayani, D., Nasution, M., & Simanjorang, E. F. S. (2019). Sentiment analysis of traveloka app using Naïve Bayes Classifier method. International Journal of Scientific and Technology Research, 8(7), 786–788. https://doi.org/10.31227/osf.io/2dbe4
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Published
2022-12-09
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[1]
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, vol. 15, no. 2, pp. 456–464, Dec. 2022.
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