Jurnal SINTA 2
Analisis Sentimen Kebijakan New Normal dengan Menggunakan Automated Lexicon Senti N-Gram
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 10 No. 1 (2023) · pp. 29–34
- ISSN
- 2528-6579, 2355-7699
- Published
- 2023-02-28
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
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. AbstractIn 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.
Collaboration network of the authors
28 authors · 52 co-authorship links · 5 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Institut Teknologi Sepuluh Nopember
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Candra Dewi | University of Brawijaya | 2 |
| Natasya Eldha Oktaviana | University of Brawijaya | 2 |
| Agus Zainal Arifin | Institut Teknologi Sepuluh Nopember | 1 |
| Evy Kamilah Ratnasari | Institut Teknologi Sepuluh Nopember | 1 |
| Siti Mutrofin | Institut Teknologi Sepuluh Nopember | 1 |
| Fadhil Yusuf Rahadika | University of Brawijaya | 1 |
| Novanto Yudistira | University of Brawijaya | 1 |
| Chastine Fatichah | Institut Teknologi Sepuluh Nopember | 1 |
| Ratih Kartika Dewi | University of Brawijaya | 1 |
| Putra Pandu Adikara | University of Brawijaya | 1 |
| Sigit Adinugroho | University of Brawijaya | 1 |
| Andri Santoso | University of Brawijaya | 1 |
| Nadia Artha Dewi | University of Brawijaya | 1 |
| Yoke Kusuma Arbawa | University of Brawijaya | 1 |
| Bayu Rahayudi | University of Brawijaya | 1 |
| Rizky Maulana Iqbal | University of Brawijaya | 1 |
| Edy Santoso | University of Brawijaya | 1 |
| Raditya Rinandyaswara | University of Brawijaya | 1 |
| Muhammad Tanzil Furqon | University of Brawijaya | 1 |
| Alfisyar Jefry Pranata | University of Brawijaya | 1 |
| Edy Santoso | University of Brawijaya | 1 |
| Fitra Abdurrachman Bachtiar | University of Brawijaya | 1 |
| Aisyah Awalina | University of Brawijaya | 1 |
| Rangga Adi Satria | University of Brawijaya | 1 |
| Sutrisno Sutrisno | University of Brawijaya | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.