Cryptocurrency Sentiment Classification Based on Comments On Facebook Using K-Nearest Neighbor

Ramu Will Sandra(1*), Yelfi Vitriani(2), Muhammad Affandes(3), Suwanto Sanjaya(4),

(1) Universitas Islam Negeri Sultan Syarif Kasim
(2) Universitas Islam Negeri Sultan Syarif Kasim
(3) Universitas Islam Negeri Sultan Syarif Kasim
(4) Universitas Islam Negeri Sultan Syarif Kasim
(*) Corresponding Author

Abstract


Cryptocurrencies continue to develop and have received world attention, price changes that occur every day are influenced by uncertain factors such as political problems and global economic problems. The author will explore the problems discussed by the public regarding positive and negative cryptocurrency comments on Facebook comments using the K-Nearest Neighbor method. This study uses 1000 data comments which are divided into 500 positive data and 500 negative data. The data was obtained manually by using the keyword "bitcoin price" on social media facebook. The results of the testing process using the confusion matrix get the highest accuracy at a comparison of 90: 10 by 62%, recall 70%, error rate 38% and precision 60,34% with k value of 11 and threshold 9.

Full Text:

Pdf

References


M. Milutinović, “Cryptocurrency,” Ekonomika, vol. 64, no. 1, pp. 105–122, 2018, doi: 10.5937/ekonomika1801105m.

A. M. Kaplan and M. Haenlein, “Users of the world, unite! The challenges and opportunities of Social Media,” Bus. Horiz., vol. 53, no. 1, pp. 59–68, 2010, doi: 10.1016/j.bushor.2009.09.003.

Hootsuite Media, “Hootsuite.” 2017.

Liu, Synthesis Lectures on Human Language Technologies,. 2012.

A. Deviyanto and M. D. R. Wahyudi, “Penerapan Analisis Sentimen Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 3, no. 1, p. 1, 2018, doi: 10.14421/jiska.2018.31-01.

J. A. Septian, T. M. Fachrudin, and A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor,” J. Intell. Syst. Comput., vol. 1, no. 1, pp. 43–49, 2019, doi: 10.52985/insyst.v1i1.36.

R. Azhar, A. Surahman, and C. Juliane, “Analisis Sentimen Terhadap Cryptocurrency Berbasis Python TextBlob Menggunakan Algoritma Naïve Bayes,” J. Sains Komput. Inform., vol. 6, pp. 267–281, 2022.

S. Sihombing, M. Rizky Nasution, and I. Sadalia, “Analisis Fundamental Cryptocurrency terhadap Fluktuasi Harga: Studi Kasus Tahun 2019-2020,” J. Akuntansi, Keuangan, dan Manaj., vol. 2, no. 3, pp. 213–224, 2021, doi: 10.35912/jakman.v2i3.373.

M. W. Pertiwi, “Analisis Sentimen Opini Publik Mengenai Sarana dan Transportasi Mudik Tahun 2019 Pada Twitter Menggunakan Algoritma Naïve Bayes, Neural Network, K-NN dan SVM,” Inti Nusa Mandiri, vol. 14, no. 1, pp. 27–32, 2019.

O. Somantri and D. Dairoh, “Analisis Sentimen Penilaian Tempat Tujuan Wisata Kota Tegal Berbasis Text Mining,” J. Edukasi dan Penelit. Inform., vol. 5, no. 2, p. 191, 2019, doi: 10.26418/jp.v5i2.32661.

E. Prasetyo, “Data Mining Konsep dan aplikasi menggunakan matlab,” Yogyakarta Andi, 2012.

M. P. Simatupang and D. P. Utomo, “Analisa Testimonial Dengan Menggunakan Algoritma Text Mining Dan Term Frequency- Inverse Document Frequence (Tf-Idf) Pada Toko Allmeeart,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 808–814, 2019, doi: 10.30865/komik.v3i1.1697.

A. Jumadi, N. N. Istiqomah, and M. N. Tentua, “Klasifikasi Evaluasi Asisten Pengajar dengan Menggunakan Metode KNN dan Naive Bayes,” Semin. Nas. Din. Inform., pp. 60–62, 2020.

S. M.Thampi et al., Intelligent Systems, Technologies and Applications: Proceedings of ISTA 2018. singapore, 2018.

K. S. Nugroho, “Confusion Matrix Untuk Evaluasi Model Pada Supervised Learning,” 2019.




DOI: https://doi.org/10.30645/ijistech.v6i2.237

Refbacks

  • There are currently no refbacks.







Jumlah Kunjungan:

View My Stats

Published Papers Indexed/Abstracted By: