Analisis Sentimen Pinjaman Online Di Media Sosial Twitter Menggunakan Metode Naive Bayes
DOI:
https://doi.org/10.30865/klik.v3i6.936Keywords:
Online Loans; Naive Bayes; Sentiment Analysis; TwitterAbstract
Sentiment analysis is conducted to measure public opinion tendencies towards ongoing or past events. One of the cases analyzed in this study is Online Loans, commonly known as "Pinjol" in Indonesian. The research data regarding Online Loans was collected from the Twitter social media platform using the keyword "Pinjaman Online." The analysis method employed in this study is Naïve Bayes. Prior to the sentiment analysis process, text data was obtained through crawling from the Twitter API using the Rapidminer application. The data was then subjected to text pre-processing. Subsequently, the data underwent TF-IDF weighting. The results of this research demonstrate the tendencies of positive and negative sentiment conflicts within each tweet discussed by Twitter users regarding Online Loans. The conclusion drawn from the sentiment analysis using the Naïve Bayes classification algorithm with data obtained from Twitter concerning Online Loans is as follows: Out of the 2931 data used, after undergoing text pre-processing, a total of 2912 data were available. Among them, negative sentiment accounted for 68.61% with 1998 data, while positive sentiment accounted for 31.39% with 914 data. The sentiment analysis of Twitter users regarding Online Loans achieved an accuracy rate of 80%.
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I. A. K. S. B. Rahardjo, Budi; Khairul, “Pengaruh Financial Technology (Fintech) Terhadap Perkembangan UMKM di Kota Magelang,” Pros. Semin. Nas. Dan Call Pap. Fak. Ekon. Univ. Tidar, pp. 347–356, 2019.
R. Wati, S. Ernawati, P. Studi, S. Informasi, U. Bina, S. Informatika, P. Studi, S. Informasi, U. Nusa, and J. Timur, “Analisis Sentimen Persepsi Publik Mengenai PPKM Pada Twitter Berbasis SVM Menggunakan Python,” vol. 06, pp. 240–247, 2021.
L. Ardiani, H. Sujaini, and T. Tursina, “Implementasi Sentiment Analysis Tanggapan Masyarakat Terhadap Pembangunan di Kota Pontianak,” J. Sist. dan Teknol. Inf., vol. 8, no. 2, p. 183, 2020.
A. Mustofa and R. Novita, “Klasifikasi Sentimen Masyarakat Terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat Menggunakan Text Mining Pada Twitter,” Build. Informatics, Technol. Sci., 2022.
A. Kusuma and A. Nugroho, “Analisa Sentimen Pada Twitter Terhadap Kenaikan Tarif Dasar Listrik Dengan Metode Naïve Bayes,” J. Ilm. Teknol. Inf. Asia, vol. 15, no. 2, p. 137, Dec. 2021.
A. Perdana, A. Hermawan, and D. Avianto, “Analisis Sentimen Terhadap Isu Penundaan Pemilu di Twitter Menggunakan Naive Bayes Clasifier,” J. Sisfokom (Sistem Inf. dan Komputer), 2022.
N. P. G. Naraswati, R. Nooraeni, D. C. Rosmilda, D. Desinta, F. Khairi, and R. Damaiyanti, “Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification,” SISTEMASI, 2021.
M. Yunus, M. Husni, and M. M. Mufadhdhal, “Klasifikasi Sentimen Terhadap Badan Penyelenggara Jaminan Sosial (BPJS) Pada Media Sosial Twitter Menggunakan Naive Bayes,” SMATIKA J., 2021.
A. Pramitha, E. Supriyati, and T. Listyorini, “Sentimen Analisis Terhadap Kaum Gay Pada Twitter Menggunakan Metode Naive Bayes,” J. Inf. Eng. Educ. Technol., vol. 5, no. 1, pp. 1–4, 2021.
M. Syarifuddin, “Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn,” Inti Nusa Mandiri, vol. 15, no. 1, pp. 23–28, 2020.
F. Ratnawati, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,” INOVTEK Polbeng - Seri Inform., vol. 3, no. 1, p. 50, 2018.
C. Prianto, N. H. Harani, and I. Firmansyah, “Analisis Sentimen Terhadap Kandidat Presiden Republik Indonesia Pada Pemilu 2019 di Media Sosial Twitter,” J. Media Inform. Budidarma, vol. 3, no. 4, p. 405, 2019.
Maiti and Bidinger, “Implementasi Teknik Web Scraping Dan Klasifikasi Sentimen Menggunakan Metode Naïve Bayes Classifier Dan Asosiasi Teks,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2017.
P. Nurmawati, E. Supriyati, and T. Listyorini, “Analisis Sentimen Terhadap Penggemar Kpop Di Media Sosial Twitter Menggunakan Naive Bayes (Studi Kasus Penggemar Grup Bts),” JIEET (Journal Inf. Eng. Educ. Technol., vol. 4, no. 2, pp. 86–89, 2020.
A. Z. Amrullah, A. Sofyan Anas, and M. A. J. Hidayat, “Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square,” Jurnal, vol. 2, no. 1, pp. 40–44, 2020.
W. Yulita, E. D. Nugroho, and M. H. Algifari, “Analisis Sentimen Terhadap Opini Masyarakat Tentang Vaksin Covid-19 Menggunakan Algoritma Naïve Bayes Classifier,” Ejurnal.Teknokrat.Ac.Id, vol. 2, no. 2, pp. 1–9, 2021.
A. C. Sitepu, W. Wanayumini, and Z. Situmorang, “Analisis Kinerja Support Vector Machine dalam Mengidentifikasi Komentar Perundungan pada Jejaring Sosial,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 475, 2021.
D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional,” J. Tekno Kompak, vol. 15, no. 1, p. 131, 2021.
E. S. Basryah, A. Erfina, C. Warman, D. Digital, and P. Store, “Analisis Sentimen Aplikasi Dompet Digital Di Era 4 . 0 Pada Masa Pendemi Covid-19 Di Play Store,” pp. 189–196, 2021.
S. Syarli and A. Muin, “Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi),” J. Ilm. Ilmu Komput., vol. 2, no. 1, pp. 22–26, 2016.
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