Analisis Sentimen Kebijakan New Normal dengan Menggunakan Automated Lexicon Senti N-Gram

Penulis

  • Rifki Akbar Siregar Universitas Brawijaya, Malang
  • Yuita Arum Sari Universitas Brawijaya, Malang
  • Indriati Indriati Universitas Brawijaya, Malang

DOI:

https://doi.org/10.25126/jtiik.20231015006

Abstrak

Dalam menghadapi pandemi COVID-19 ini, pemerintah Indonesia mengeluarkan beberapa kebijakan di antaranya adalah Pembatasan Sosial Berskala Besar, dan New normal. Kebijakan New normal ini kemudian menjadi ramai diperbincangkan oleh masyarakat. Analisis sentimen dari opini yang beredar terkait isu tersebut dapat dilakukan sehingga pemerintah dapat mengevaluasi kebijakan tersebut. Dalam penelitian ini diusulkan menggunakan Lexicon Senti-N-Gram untuk analisis sentimen dengan tujuan untuk mengetahui pengaruh Lexicon Senti-N-Gram pada analisis sentimen Bahasa Indonesia. Adapun penelitian ini menggunakan data sebanyak 350 data tweet yang terbagi menjadi 229 tweet kelas positif dan 121 tweet kelas negatif. Hasil evaluasi yang diperoleh dengan menggunakan data dengan stemming lebih tinggi dibandingkan dengan data tanpa stemming. Hasil pengujian kinerja sistem terhadap lexicon Senti-N-Gram mendapatkan nilai accuracy sebesar 63,42%, precision sebesar 77%, recall sebesar 62,88%, dan f-measure sebesar 69,23% dengan nilai rata-rata kappa antar Annotator sebesar 0.5395 untuk data yang melalui proses stemming.  Berdasarkan hasil pengujian yang telah diperoleh dapat disimpulkan bahwa proses stemming serta proses translasi kata satu per satu yang dilakukan dapat memengaruhi kata berdasarkan konteksnya.

 

Abstract

In dealing with the COVID-19 pandemic, the Indonesian government has issued several policies, including Large-Scale Social Restrictions and New normal. The New normal policy then became widely discussed by the public. Sentiment analysis of the opinions circulating on this issue can be carried out so that the government can evaluate the policy. In this study, it is proposed to use the Lexicon Senti-N-Gram for sentiment analysis in order to determine the effect of the Lexicon Senti-N-Gram on Indonesian sentiment analysis. The research used 350 tweets, which were divided into 229 positive class tweets and 121 negative class tweets. The evaluation results obtained using stemming data were higher than those without stemming. The results of the system performance test of the Lexicon Senti-N-Gram obtained an accuracy value of 63.42%, 77% precision, 62.88% recall, and 69.23% f-measure with an average kappa value between Annotators of 0.5395 for data that goes through the stemming process. Based on the test results that have been obtained, it can be concluded that the stemming process and the process of translating words one by one can affect words based on their context.


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Referensi

AGARWAL, S. 2014. Data mining: Data mining concepts and techniques. In Proceedings - 2013 International Conference on Machine Intelligence Research and Advancement, ICMIRA 2013. https://doi.org/10.1109/ICMIRA.2013.45

ANWAR, F. 2020. Update Corona Indonesia 15 Agustus: Tambah 2.345, Total 137.468 Kasus. Health.Detik.Com.

https://health.detik.com/berita-detikhealth/d-5134642/update-corona-indonesia-15-agustus-tambah-2345-total-137468-kasus

DEY, A., JENAMANI, M., & THAKKAR, J. J. 2018. Senti-N-Gram: An n-gram lexicon for sentiment analysis. Expert Systems with Applications, 103, 92–105. https://doi.org/10.1016/j.eswa.2018.03.004

FITRI NIASITA, A., ADIKARA, P. P., & ADINUGROHO, S. 2019. Analisis Sentimen Pembangunan Infrastruktur di Indonesia dengan Automated Lexicon Word2Vec dan Naive-Bayes. J-Ptiik, 3(3), 2673–2679. http://j-ptiik.ub.ac.id

HUTTO, C.J. and GILBERT, E. 2014. VADER: A Parsimonious Rule-based Model for. Eighth International AAAI Conference on Weblogs and Social Media, 18. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewPaper/8109

INDRIATI, I., & RIDOK, A. 2016. Sentiment Analysis for Review Mobile Applications Using Neighbor Method Weighted K-Nearest Neighbor (Nwknn). Journal of Enviromental Engineering and Sustainable Technology, 3(1), 23–32. https://doi.org/10.21776/ub.jeest.2016.003.01.4

JACKOWAY, A., SAMET, H., & SANKARANARAYANAN, J. 2011. Identification of live news events using Twitter. 3rd ACM

SIGSPATIAL International Workshop on Location-Based Social

Networks, LBSN 2011 - Held in Conjunction with the 19th ACM

SIGSPATIAL GIS 2011. https://doi.org/10.1145/2063212.2063224

KIRITCHENKO, S., & MOHAMMAD, S. M. 2016. Happy Accident: A sentiment composition lexicon for opposing polarity phrases. Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016, 1157–1164.

LIN, Y., WANG, X., & ZHOU, A. 2016. Opinion spam detection. In Opinion Analysis for Online Reviews (Issue May). https://doi.org/10.1142/9789813100459_0007

ORGANIZATION, W. H. 2020. Pertanyaan dan jawaban terkait Coronavirus. Www.Who.Int. https://www.who.int/indonesia/news/novel-coronavirus/qa/qa-for-public

PUTSANRA, D. V. 2020. Arti New Normal Indonesia: Tatanan Baru Beradaptasi dengan COVID-19. Www.Tirto.Id. https://tirto.id/arti-new-normal-indonesia-tatanan-baru-beradaptasi-dengan-covid-19-fDB3

ROFIQOH, U., PERDANA, R. S., & FAUZI, M. A. 2017. Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexion Based Feature. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 1(12), 1725–1732. http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/628

Diterbitkan

28-02-2023

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Ilmu Komputer

Cara Mengutip

Analisis Sentimen Kebijakan New Normal dengan Menggunakan Automated Lexicon Senti N-Gram. (2023). Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(1), 29-34. https://doi.org/10.25126/jtiik.20231015006