The Tweetology of New and Renewable Energy in Indonesia

https://doi.org/10.22146/ijccs.81397

Ariana Yunita(1*), Sara Florensia Telaumbanua(2), Ade Irawan(3)

(1) Department of Computer Science, Universitas Pertamina, Jakarta
(2) Department of Computer Science, Universitas Pertamina, Jakarta
(3) Department of Computer Science, Universitas Pertamina, Jakarta
(*) Corresponding Author

Abstract


The amount of unstructured data is increasing annually, which is promising for
gaining insights. Twitter, a platform producing unstructured data, is currently one of the most
popular media platforms used for conducting research on a topic's trend. This study attempts to
analyze the topic of New and Renewable Energy (NRE) in Indonesia. The purpose of this study
is to gain insights into the NRE topic trend over the last ten years by modeling the topics
discussed on Twitter and examining the location distribution of users who post tweets about the
topic. Accordingly, this study employed descriptive analysis, geocoding analysis, and topic
modeling. The results of descriptive analysis show that the development of NRE has accelerated
in recent years, particularly in 2021. Geocoding analysis reveals that the distribution of people
who engage in NRE posting activities is dominated by DKI Jakarta province. Topic modeling
yielding two topics that were discussed the most by Indonesians over a 10-year period. The two
topics are related to government policies that support the development of NRE and electricity,
which is Indonesia's focus in NRE. This study highlights the importance of analyzing the
Tweetology of NRE.


Keywords


Text mining; New and renewable energy; Twitter; Geocoding analysis; Topic modeling

Full Text:

PDF


References

[1] Deleted for Peer Review

[2] D. R.-J. G.-J. Rydning and others, “The digitization of the world from edge to core,” Framingham: International Data Corporation, 2018. [Online]. Available: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf. [Accessed: 25-Dec-2021].

[3] Deleted for Peer Review

[4] L. Ngamassi, H. Shahriari, T. Ramakrishnan, and S. Rahman, “Text mining hurricane harvey tweet data: Lessons learned and policy recommendations,” Int. J. Disaster Risk Reduct., vol. 70, p. 102753, 2022.

[5] Z. A. Hasibuan, “Towards using universal big data in artificial intelligence research and development to gain meaningful insights and automation systems,” in 2020 International Workshop on Big Data and Information Security, IWBIS 2020, 2020, pp. 9–15.

[6] S. Kemp, “DIGITAL 2022: INDONESIA,” 2022. [Online]. Available: https://datareportal.com/reports/digital-2022-indonesia. [Accessed: 23-Oct-2022].

[7] M. Ahlgren, “50+ Twitter statistics and facts for 2022.” [Online]. Available: https://www.websiterating.com/research/twitter-statistics/. [Accessed: 16-Sep-2022].

[8] G. Fitzgerald and M. FitzGibbon, “A Comparative analysis of traditional and digital data collection methods in social research in LDCs-Case Studies Exploring Implications for Participation, Empowerment, and (mis) Understandings,” IFAC Proc. Vol., vol. 47, no. 3, pp. 11437–11443, 2014.

[9] United Nations Development Programme, “Do you know all 17 SDGs?” [Online]. Available: https://sdgs.un.org/goals. [Accessed: 23-Oct-2022].

[10] Direktorat Jenderal Kekayaan Negara (DJKN), “Bauran Energi Baru Terbarukan Ditargetkan 23 Persen di 2025,” 2022. .

[11] V. N. Setiawan, “Capaian Bauran Energi Hijau Juni 2022 Baru 12,8%.” [Online]. Available: https://www.cnbcindonesia.com/news/20220701174802-4-352304/capaian-bauran-energi-hijau-juni-2022-baru-128. [Accessed: 23-Oct-2022].

[12] M. Khalil, J. Wong, E. Er, M. Heitmann, and G. Belokrys, “Tweetology of Learning Analytics: What does Twitter tell us about the trends and development of the field?,” in LAK22: 12th International Learning Analytics and Knowledge Conference, 2022, pp. 347–357.

[13] Z. C. Steinert-Threlkeld, Twitter as data. Cambridge University Press, 2018.

[14] W. Ahmed, P. A. Bath, and G. Demartini, “Using Twitter as a data source: An overview of ethical, legal, and methodological challenges,” ethics online Res., vol. 2, pp. 79–107, 2017.

[15] M. Allahyari et al., “A brief survey of text mining: Classification, clustering and extraction techniques,” in Proceedings of KDD Bigdas, 2017.

[16] U. Chauhan and A. Shah, “Topic modeling using latent Dirichlet allocation: A survey,” ACM Comput. Surv., vol. 54, no. 7, pp. 1–35, 2021.

[17] S. Che, D. Nan, P. Kamphuis, and J. H. Kim, “A comparative analysis of attention to facial recognition payment between China and South Korea: a news analysis using Latent Dirichlet allocation,” in International Conference on Human-Computer Interaction, 2021, pp. 75–82.

[18] E. Laoh, I. Surjandari, and L. R. Febirautami, “Indonesians’ Song Lyrics Topic Modelling Using Latent Dirichlet Allocation,” in 2018 5th International Conference on Information Science and Control Engineering (ICISCE), 2018, pp. 270–274.

[19] R. P. F. Afidh and Z. A. Hasibuan, “Indonesia’s News Topic Discussion about Covid-19 Outbreak using Latent Dirichlet Allocation,” in 2020 Fifth International Conference on Informatics and Computing (ICIC), 2020, pp. 1–6.

[20] E. Zosa and M. Granroth-Wilding, “Multilingual dynamic topic model,” RANLP 2019-Natural Lang. Process. a Deep Learn. World, 2019.

[21] P. Brzustewicz and A. Singh, “Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA,” Energies, vol. 14, no. 18, p. 5787, 2021.

[22] A. Pons, C. Vintrò, J. Rius, and J. Vilaplana, “Impact of Corporate Social Responsibility in mining industries,” Resour. Policy, vol. 72, p. 102117, 2021.

[23] E. De Santis, A. Martino, and A. Rizzi, “An infoveillance system for detecting and tracking relevant topics from Italian tweets during the COVID-19 event,” Ieee Access, vol. 8, pp. 132527–132538, 2020.

[24] B. Chae and E. Park, “Corporate social responsibility (CSR): A survey of topics and trends using Twitter data and topic modeling,” Sustainability, vol. 10, no. 7, p. 2231, 2018.

[25] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, no. Jan, pp. 993–1022, 2003.

[26] Z. Epstein, “BlackBerry lost 4 million subscribers in Q1 despite new launches,” 2013. [Online]. Available: https://bgr.com/general/blackberry-subscribers-q1-2014/. [Accessed: 23-Oct-2022].

[27] N. Wilantika, D. I. Sensuse, S. B. Wibisono, P. L. Putro, and A. Damanik, “Grouping of provinces in Indonesia according to digital divide index,” in 2018 6th International Conference on Information and Communication Technology, ICoICT 2018, 2018, no. c, pp. 380–388.

[28] Deleted for Peer Review

[29] M. Maulidia, P. Dargusch, P. Ashworth, and F. Ardiansyah, “Rethinking renewable energy targets and electricity sector reform in Indonesia: A private sector perspective,” Renew. Sustain. Energy Rev., vol. 101, pp. 231–247, 2019.



DOI: https://doi.org/10.22146/ijccs.81397

Article Metrics

Abstract views : 732 | views : 564

Refbacks

  • There are currently no refbacks.




Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2