Sentiment Analysis Terhadap Kinerja Mentri Kesehatan Indonesia Selama Pandemi COVID-19

Authors

  • Tri Rivanie Stmik Nusa Mandiri Jakarta

DOI:

https://doi.org/10.33481/infomans.v15i2.201

Keywords:

Support Vector Machine, Naïve Bayes, Sentiment Analisis, k-Fold Cross Validation

Abstract

Pandemi di Indonesa belum juga mereda dan terkendali, Mentri kesehatan Indonesia, Dr. dr. Terawan Agus Purtanto, Sp.Rad adalah sosok yang dianggap paling tepat bertanggung jawab memberi penjelasan mengenai situasi sebenarnya dan sejauh apa penanganan yang sudah dilakukan Negara namun kini beliau sangat jarang tampil di hadapan publik untuk menjelaskan mengenai penanganan pandemi Covid-19, Menanggapi hal tersebut banyak masyarakat yang pro dan kontra ikut memberikan pendapat dan tanggapan. Meningkatknya penggunaan internet selama masa pandemic terutama sosial media, menjadi sarana menuangkan pendapat, dimana Salah satunya adalah twitter. memposting tweet merupakan kebiasaan masyarkat untuk menilai atau menanggapi peristiwa mereprensentasikan tanggapan masyarakat terhadap suatu peristiwa, terutama langkah dan kebijakan kementrian kesehatan dalam penanganan dan pemutusan rantai wabah epidemic Covid-19. Melalui postingan tweet tersebut peneliti menganalisis dan membatasi tweet berbahasa Indonesia yang berkaitan dengan Kinerja Pemerintah, Khususnya Kementrian Kesehatan tersebut atau disebut analisis sentiment. Dengan membandingkan 2 metode Support Vector Machine (SVM) dan Naïve Bayes(NB). Pengujian dilakukan menggunakan k-Fold Cross Validation untuk memperoleh nilai akurasi (accuracy). Hasil pengujian cross validation SVM dan NB mendapatkan nilai accuracy 71,71% dan 66,45% dengan nilai Area Under the Curve (AUC) 0,817 dan 0,499. Hasil dari pengujian kedua algoritma tersebut nilai. accuracy tertinggi adalah SVM sebesar 71,71%

Downloads

Download data is not yet available.

References

M. Kamyab, R. Tao, M. H. Mohammadi, and A. Rasool, “Sentiment analysis on Twitter: A text mining approach to the Afghanistan status reviews,” ACM Int. Conf. Proceeding Ser., pp. 14–19, 2018, doi: 10.1145/3293663.3293687.

J. Weng, E.-P. Lim, J. Jiang, and Z. qi, Twitterrank: Finding Topic-Sensitive Influential Twitterers. 2010.

J. Goldenberg and M. Fresko, “Mine Your Own Business?: Market-Structure Surveillance Through Text Mining,” vol. 31, no. 3, pp. 521–543, 2012.

B. Liu, “Sentiment Analysis and Subjectivity,” pp. 1–38, 2010.

R. Jannati, R. Mahendra, C. W. Wardhana, and M. Adriani, “Stance Classification Towards Political Figures on Blog Writing Stance Classification towards Political Figures on Blog Writing,” no. February 2019, 2018, doi: 10.1109/IALP.2018.8629144.

B. Pratama, D. D. Saputra, D. Novianti, and E. P. Purnamasari, “Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM And NB Methods Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM And NB Methods,” 2019, doi: 10.1088/1742-6596/1201/1/012038.

A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of Sentimental Reviews Using Machine Learning Techniques,” Procedia - Procedia Comput. Sci., vol. 57, pp. 821–829, 2015, doi: 10.1016/j.procs.2015.07.523.

S. Kurniawan, W. Gata, D. A. Puspitawati, M. Tabrani, and K. Novel, “Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada,” vol. 1, no. 10, pp. 2–8, 2021.

A. Fauzi, A. N. Rais, M. F. Akbar, and W. Gata, “ANALISIS SENTIMEN BERINTERNET PADA MEDIA SOSIAL NAIVE BAYES,” pp. 46–54, 2018.

N. Friedman, M. Linial, and D. Pe, “Using Bayesian Networks to Analyze Expression Data,” pp. 127–135.

I. Chaturvedi, E. Ragusa, P. Gastaldo, R. Zunino, and E. Cambria, “Bayesian Network based Extreme Learning Machine for Subjectivity Detection,” J. Franklin Inst., 2017, doi: 10.1016/j.jfranklin.2017.06.007.

C. Series, “K-nearest neighbor analysis to predict the accuracy of product delivery using administration of raw material model in the cosmetic industry ( PT K-nearest neighbor analysis to predict the accuracy of product delivery using administration of raw material model in the cosmetic industry ( PT Cedefindo ),” 2019, doi: 10.1088/1742-6596/1367/1/012008.

L. Dey, “Sentiment Analysis of Review Datasets using Naïve Bayes ’ and K -NN Classifier.”

N. D.?; Putranti and E. Winarko, “Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine,” vol. 8, no. 1, pp. 91–100, 2014.

G. Mann, R. Mcdonald, and N. Silberman, “Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models,” pp. 1–9.

A. Severyn, “Twitter Sentiment Analysis with Deep Convolutional Neural Networks,” pp. 959–962.

M. G. Santos, C´?cero Nogueira dos, “Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts,” pp. 69–78, 2014.

H. A. Putranto, O. Setyawati, and A. L. Belakang, “Pengaruh Phrase Detection dengan POS -Tagger terhadap Akurasi Klasifikasi Sentimen menggunakan SVM,” vol. 5, no. 4, pp. 252–259, 2016.

A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support Vector Machine,” 2003.

J. Chen, H. Huang, S. Tian, and Y. Qu, “Feature Selection for Text Classification with Na"{i}ve Bayes,” Expert Syst. Appl., vol. 36, no. 3, pp. 5432–5435, Apr. 2009, doi: 10.1016/j.eswa.2008.06.054.

Q. Ye, Z. Zhang, and R. Law, “Expert Systems with Applications Sentiment classification of online reviews to travel destinations by supervised machine learning approaches,” Expert Syst. Appl., vol. 36, no. 3, pp. 6527–6535, 2009, doi: 10.1016/j.eswa.2008.07.035.

A. Fern and S. Garc, “SMOTE for Learning from Imbalanced Data?: Progress and Challenges , Marking the 15-year Anniversary,” vol. 61, pp. 863–905, 2018

Published

2021-11-20

How to Cite

Tri Rivanie. (2021). Sentiment Analysis Terhadap Kinerja Mentri Kesehatan Indonesia Selama Pandemi COVID-19. Infoman’s : Jurnal Ilmu-Ilmu Manajemen Dan Informatika, 15(2). https://doi.org/10.33481/infomans.v15i2.201

Issue

Section

Articles