바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Sentiment Analysis on Indonesia Economic Growth using Deep Learning Neural Network Method

The Journal of Industrial Distribution & Business / The Journal of Industrial Distribution & Business, (E)2233-5382
2022, v.13 no.6, pp.9-18
https://doi.org/https://doi.org/10.13106/jidb.2022.vol13.no6.9
KRISMAWATI, Dewi
MARIEL, Wahyu Calvin Frans
ARSYI, Farhan Anshari
PRAMANA, Setia

Abstract

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.

keywords
Sentiment, Analysis, Economic, Growth, Neural Network

Reference

1.

Alamsyah, A., & Syawiluna, M. (2021). Mapping Organization Knowledge Network and Social Media Based Reputation Management. arXiv preprint arXiv:2102.12337.

2.

Blazquez, D., & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130, 99-113.

3.

BPS. (2021). Berita Resmi Statistik Pertumbuhan Ekonomi (Produk Domestik Bruto).

4.

Boshkoska, M., & Jankulovski, N. (2020). Coronavirus Impact on Global Economy. Annals of the „Constantin Brâncuşi”University of Târgu Jiu, Economy Series,, 4, 18-23.

5.

Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. INTEGER: Journal of Information Technology, 2(1).

6.

Paramartha, D. Y., Deli, N. F., Fitriyani, A. L., & Pramana, S. (2021). Tourism Resilience Process During Pandemic with Big Data Insight. Jurnal Ekonomi Indonesia, 10(3), 239-255.

7.

Dikti. Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi. (2020). Tantangan Dunia Pendidikan di Masa Pandemi. Oktober 2020.

8.

Enviha. (2021). Limbah Medis Saat Covid-19. Fakultas Kesehatan Masyarakat Universitas Indonesia. 2021.

9.

Euge Inzaugarat. (2019). Visualizing Twitter interactions with NetworkX. 2019.

10.

Firmansyah, A. F. B., & Pramana, S. (2018). Ensemble based gustafson kessel fuzzy clustering. Journal of Data Science and Its Applications, 1(1), 1-9.

11.

Rachman, F. F., & Pramana, S. (2021). Analysis of Indonesian People's Sentiments About the Side Effects of the COVID-19Vaccine on Twitter. Journal of Data Science and Its Applications, 4(1), 1-10.

12.

Rachman, F. F., & Pramana, S. (2020). Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter. Indonesian of Health Information Management Journal (INOHIM), 8(2), 100-109.

13.

Hadiana, A. (2018). Designing interface of mobile parental information system based on users’ perception using Kansei engingeering. Journal of Data Science and Its Applications, 1(1), 10-19.

14.

Manuel, B., & Tricahyono, D. (2018). Classifying electronic word of mouth and competitive position in online game industry. Journal of Data Science and Its Applications, 1(1), 20-27.

15.

Iskandar, A. R., & Purno, A. (2018). Transition Strategies of Change Management For the Succesful Implementation of Data Warehouse of Higher Education in Indonesia. Journal of Data Science and Its Applications, 1(1), 28-38.

16.

Kemenko. (2021). Pertumbuhan Ekonomi Triwulan II 2021Menembus Zona Ekspansif. Kementrian Koordinator Bidang Ekonomi. 2021.

17.

Kemenkeu. (2022). Stimulus Covid-19 Diskon Listrik Diperpanjang. Kementian Keuangan. 2022.

18.

Pramana, S., Paramartha, D. Y., Ermawan, G. Y., Deli, N. F., & Srimulyani, W. (2021). Impact of COVID-19 pandemic on tourism in Indonesia. Current Issues in Tourism, 1-21.

19.

Simanjuntak, T. N., & Pramana, S. (2021). Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia. Indonesian Journal of Statistics and Its Applications, 5(2), 304-313.

20.

Wahid, D. H., & Azhari, S. N. (2016). Peringkasan sentimen esktraktif di twitter menggunakan hybrid TF-IDF dan cosine similarity. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 10(2), 207-218.

21.

Mariel, W. C. F., Mariyah, S., & Pramana, S. (2018, March). Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text. In Journal of Physics: Conference Series (Vol. 971, No.1, p. 012049). IOP Publishing

The Journal of Industrial Distribution & Business