Analyzing Main Topics Regarding The Electronic Information and Transaction Act in Instagram Using Latent Dirichlet Allocation

Hans Kresnawan; Sola Graciana Felle; Hanna Gloria Mokay; Nur Aini Rakhmawati

doi:10.32734/jocai.v5.i2-6125

Authors

Hans Kresnawan Institut Teknologi Sepuluh Nopember https://orcid.org/0000-0003-2567-3001
Sola Graciana Felle Institut Teknologi Sepuluh Nopember https://orcid.org/0000-0003-3772-0100
Hanna Gloria Mokay Institut Teknologi Sepuluh Nopember https://orcid.org/0000-0002-1370-5822
Nur Aini Rakhmawati Institut Teknologi Sepuluh Nopember https://orcid.org/0000-0002-1321-4564

DOI:

https://doi.org/10.32734/jocai.v5.i2-6125

Keywords:

Instagram, Latent Dirichlet Allocation, Text Mining, UU ITE, Topic Analysis

Abstract

Indonesia is currently experiencing its fourth industrial revolution in the 21st century. With the introduction of the internet, Indonesia is expected to gain more than a hundred billion US Dollars and twenty-six million job openings by 2030. The rising usage of information technology prompts regulators to develop The Electronic Information Transaction Act to protect the populace from cybercrime. However, the law attracts numerous criticism due to its vague interpretation. This resulted in numerous arrests of innocents throughout Indonesia. Thus, the public is trying to voice their opinions on social media for the sake of preventing any more cases in the future. The usage of Latent Dirichlet Allocation could provide numerous benefits for this research. The separation between latent topics among random mixtures helps to identify the common ground and correlation between each post. These latent topics will be elaborated with a sample post to provide insights and expectations of the public towards the law.

Downloads

Download data is not yet available.

Author Biographies

Hans Kresnawan, Institut Teknologi Sepuluh Nopember

Student

Sola Graciana Felle, Institut Teknologi Sepuluh Nopember

Student

Hanna Gloria Mokay, Institut Teknologi Sepuluh Nopember

Student

Nur Aini Rakhmawati, Institut Teknologi Sepuluh Nopember

Lecturer

References

Asosiasi Penyelenggara Jasa Internet Indonesia, “LAPORAN SURVEI INTERNET APJII 2019 – 2020 (Q2),” 2020.

Kementerian Komunikasi dan Informatika Republik Indonesia, “Rancangan Rencana Strategis Kemenkominfo 2020-2024.”

B. Galih, “Dandhy Dwi Laksono Ditangkap Polisi atas Tuduhan Menebarkan Kebencian Halaman all - Kompas.com,” 2019. https://nasional.kompas.com/read/2019/09/27/00462591/dandhy-dwi-laksono-ditangkap-polisi-atas-tuduhan-menebarkan-kebencian?page=all (accessed Apr. 09, 2021).

CNN indonesia, “Kronologi Kasus Baiq Nuril, Bermula dari Percakapan Telepon,” 2018. https://www.cnnindonesia.com/nasional/20181114133306-12-346485/kronologi-kasus-baiq-nuril-bermula-dari-percakapan-telepon (accessed Apr. 09, 2021).

D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., vol. 3, no. 4–5, pp. 993–1022, 2003, doi: 10.1016/b978-0-12-411519-4.00006-9.

A. Rahmawati, N. L. Nikmah, R. D. A. Perwira, and N. A. Rakhmawati, “Analisis topik konten channel YouTube K-pop Indonesia menggunakan Latent Dirichlet Allocation,” J. Ilm. Sist. Inf., vol. 11, no. 1, pp. 16–25, Jan. 2021, doi: 10.26594/teknologi.v11i1.2155.

J. Xue, J. Chen, C. Chen, C. Zheng, S. Li, and T. Zhu, “Public discourse and sentiment during the COVID 19 pandemic: Using latent dirichlet allocation for topic modeling on twitter,” PLoS One, vol. 15, no. 9 September, Sep. 2020, doi: 10.1371/journal.pone.0239441.

D. Fang, H. Yang, B. Gao, and X. Li, “Discovering research topics from library electronic references using latent Dirichlet allocation,” Libr. Hi Tech, vol. 36, no. 3, pp. 400–410, 2018, doi: 10.1108/LHT-06-2017-0132.

O. Toubia, G. Iyengar, R. Bunnell, and A. Lemaire, “Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption,” J. Mark. Res., vol. 56, no. 1, pp. 18–36, 2019, doi: 10.1177/0022243718820559.

Y. Guo, S. J. Barnes, and Q. Jia, “Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation,” Tour. Manag., vol. 59, pp. 467–483, 2017, doi: 10.1016/j.tourman.2016.09.009.

K. Bastani, H. Namavari, and J. Shaffer, “Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints,” Expert Syst. Appl., vol. 127, pp. 256–271, 2019, doi: 10.1016/j.eswa.2019.03.001.

S. (Sixue) Jia, “Toward a better fitness club: Evidence from exerciser online rating and review using latent Dirichlet allocation and support vector machine,” Int. J. Mark. Res., vol. 61, no. 1, pp. 64–76, 2019, doi: 10.1177/1470785318770571.

Q. Zhang, S. Liu, D. Gong, and Q. Tu, “A latent-dirichlet-allocation based extension for domain ontology of enterprise’s technological innovation,” Int. J. Comput. Commun. Control, vol. 14, no. 1, pp. 107–123, 2019, doi: 10.15837/ijccc.2019.1.3366.

Y. Wang and L. Xu, “Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation,” PeerJ, vol. 2018, no. 6, 2018, doi: 10.7717/peerj.5036.

P. Shah, D. Sharma, and R. Sekhar, “Analysis of Research Trends in Fractional Controller Using Latent Dirichlet Allocation,” Eng. Lett., vol. 29, no. 1, 2021.