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The Effect of Bot Accounts on Community Toward Online Loans on Twitter
Abstract
The massive advancement of technology is supported by the convenience and ease of transacting carried by fintech applicators, especially for the online loan (pinjol) experience is getting easier. However, the ease of it raises issues of unrest in the community, many complaints that arise such as unnatural bills, how debt collectors charge at the time of maturity, and the issue of sharing the borrower's personal data to the public. Another concern of the existence of online loans is the clarity of their legality, there are still many online loan applicators that are not registered with the Financial Services Authority (OJK). It leads to a negative attitude. The purpose of this study is to find out public sentiment towards the existence of online loans through social media Twitter. The study also wanted to find out if there were significant sentiment differences when bot accounts were in the dataset. Researchers obtained 941 lines of twitter data by not separating bot accounts. If the bot account is separated, the data becomes 432 data. After a deeper analysis, it was found that the tendency of sentiment towards online loans in the community was negative with a value of 45.6%. After separating the bot account from the dataset, the negative sentiment still dominated from the existing sentiment to 54.2%. So that it can be drawn another conclusion that the existence and absence of bot accounts in the dataset only gives a small influence on other sentiments, bot accounts do not change the initial conclusion that said public sentiment towards the existence of online loans tends to lead to negative attitudes.
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V. Juita, F. Firdaus, and T. N. P. Hermanto, “Studi Prilaku Pengguna Layanan Financial Technology (Fintech) di Indonesia: Analisa Persepsi Risiko dan Manfaat,” Jurnal Inovasi Pendidikan Ekonomi (JIPE), vol. 10, no. 2, p. 118, Nov. 2020, doi: 10.24036/011100040.
D. S. Utami and A. Erfina, “Analisis Sentimen Pinjaman Online Di Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Seminar Nasional Sistem Informasi dan Manajemen Informatika, vol. 1, no. 1, pp. 299–305, 2021, Accessed: Oct. 07, 2021. [Online]. Available: https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/34/30
K. Ilmu, P. Studi, I. Komunikasi, F. Komunikasi, D. Desain, and M. Retnasary, “Jurnal Politikom Indonesiana: Penggunaan Media Sosial Twitter Masyarakat di Kota Bandung sebagai Media untuk Memperoleh Informasi mengenai New Normal dalam Akun @humasjabar Rio Esaputra Rohman,” 2020. [Online]. Available: https://journal.unsika.ac.id/index.php/politikomindonesianahttps://journal.unsika.ac.id/index.php/politikomindonesiana18
M. I. Aditama, R. I. Pratama, K. H. U. Wiwaha, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Pengguna Media Sosial Twitter Terhadap Pengadaan Vaksin COVID-19,” Journal Information Engineering and Education Technologi, vol. 04, no. 02, 2020.
F. Rashif, G. Ihza Perwira Nirvana, M. Alif Noor, and N. Aini Rakhmawati, “Implementasi LDA untuk Pengelompokan Topik Cuitan Akun Bot Twitter bertagar #Covid-19 LDA Implementation for Topic of Bot’s Tweets with #Covid-19 Hashtag,” Cogito Smart Journal |, vol. 7, no. 1, 2021.
R. Sugangga and E. H. Sentoso, “Perlindungan Hukum Terhadap Pengguna Pinjaman Online (Pinjol) Ilegal,” 2020.
M. I. Aditama, R. I. Pratama, K. H. U. Wiwaha, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Pengguna Media Sosial Twitter Terhadap Pengadaan Vaksin COVID-19,” Journal Information Engineering and Educational Technology (JIEET), vol. 4, no. 2, 2020.
N. Muchammad Shiddieqy Hadna, P. Insap Santosa, and W. Wahyu Winarno, “Studi Literatur Tentang Perbandingan Metode untuk Proses Analisis Sentimen Di Twitter,” 2016.
J. Andreanus and A. Kurniawan, “Sejarah , Teori Dasar dan Penerapan Reinforcement Learning : Sebuah Tinjauan Pustaka,” Jurnal Telematika, vol. 12, no. 2, 2018.
T. Mutiah, I. Albar, Fitriyanto, and A.Rafiq, “Etika Komunikasi Dalam Menggunakan Media Sosial,” Global Komunika, vol. 1, no. 1, 2019.
Y. Fahrima, “Netiquette: Etika Jejaring Sosial Generasi Milenial dalam Media Sosial,” Jurnal Penelitian Pers Dan Komunikasi Pembangunan, vol. 22, pp. 69–78, 2018, doi: doi.org/10.46426/jp2kp.v22i1.82.
A. Setiadi, “Pemanfaatan media sosial untuk efektifitas komunikasi,” Jurnal Ilmiah Matrik, vol. 16, no. 1, 2014.
F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” International Conference on Computational Linguistics, vol. 28, pp. 757–770, 2020, doi: 10.18653/v1/2020.coling-main.66.
N. L. P. C. Savitri, R. A. Rahman, R. Venyutzky, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 1, Apr. 2021, doi: 10.28932/jutisi.v7i1.3216.
F. Arrizal, A. Ikhsan, and A. Cintya, “Dataset Pinjaman Online Twitter,” Zenodo, 2021.
DOI: https://doi.org/10.32520/stmsi.v11i1.1657
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