Algoritma Naïve Bayes Classifier Untuk Analisis Sentiment Pengguna Twitter Terhadap Provider By.u
DOI:
https://doi.org/10.30865/mib.v6i3.4132Keywords:
Naïve Bayes Classifier, Sentiment Analysis, Classification, Text Mining, TwitterAbstract
The development of the internet which has increased in recent years has made it easy for people to give their opinion on a product. By.u, as a new internet service provider, has made many new users share their opinions with each other. Many by.u users give their opinions through social media, especially twitter. From these problems, research was conducted using sentiment analysis. The research stages consisted of collecting data from social media Twitter, preprocessing data, weighting TF-IDF data and classifying using the Naïve Bayes Classifier algorithm. To get the best evaluation results, a comparison of training data and test data was carried out. Data classification is done automatically after cleaning the data in the preprocessing process. There are 2 labels for the data resulting from the automatic classification, namely positive and negative. The dataset after classification will be used as training data and test data. The datasets to be tested are divided into 3 numbers, namely the number of 1000 datasets, 2000 datasets, and 3000 datasets. The test was carried out 3 times for each dataset. The accuracy test is carried out using a confusion matrix. The test results with the highest accuracy were obtained by the nave Bayes classifier with a multinomial model of 85%.References
A. Shelar and C. Y. Huang, “Sentiment analysis of twitter data,†2018, doi: 10.1109/CSCI46756.2018.00252.
D. S. Pamungkas, N. A. Setiyanto, and E. Dolphina, “Analisis Sentiment Pada Sosial Media Twitter Menggunakan Naive Bayes Classifier Terhadap Kata Kunci ‘Kurikulum 2013’’,’†vol. 14, no. 4, pp. 299–314, 2015.
M. D. R. W. Wahyudi, “Analisis sentimen ujaran kebencian pemilihan presiden 2019 menggunakan algoritme Naïve Bayes,†JNANALOKA, 2020, doi: 10.36802/jnanaloka.2020.v1-no1-12.
B. M. Pintoko and K. M. L, “Analisis Sentimen Jasa Transportasi Online pada Twitter Menggunakan Metode Naïve Bayes Classifier,†e-Proceeding Eng., 2018.
D. Olivita, Y. Vitriani, J. Teknik Informatika, F. Sains dan Teknologi, U. H. Sultan Syarif Kasim Riau Jl Soebrantas No, and S. Baru, “Perbandingan Klasifikasi Tugas Akhir Mahasiswa Jurusan Teknik Informatika Menggunakan Metode Naïve Bayes Classifier dan K-Nearest Neighbor,†J. Sains, Teknol. dan Ind., vol. 14, no. 1, pp. 79–85, 2016.
U. Rofiqoh, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexion Based Feature,†J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 1, no. 12, pp. 1725–1732, 2017, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/628.
S. Budi, “Text Mining Untuk Analisis Sentimen Review Film,†Techno.COM, vol. 16, no. 1, pp. 1–8, 2017.
F. Ratnawati, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,†INOVTEK Polbeng - Seri Inform., vol. 3, no. 1, p. 50, 2018, doi: 10.35314/isi.v3i1.335.
C. C. Aggarwal and C. X. Zhai, Mining text data, vol. 9781461432234. 2013.
C. Troussas, M. Virvou, K. J. Espinosa, K. Llaguno, and J. Caro, “Sentiment analysis of Facebook statuses using Naive Bayes Classifier for language learning,†2013, doi: 10.1109/IISA.2013.6623713.
R. Feldman and J. Sanger, The Text Mining Handbook. 2006.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
R. Ferdiana, F. Jatmiko, D. D. Purwanti, A. S. T. Ayu, and W. F. Dicka, “Dataset Indonesia untuk Analisis Sentimen,†J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 4, p. 334, 2019, doi: 10.22146/jnteti.v8i4.533.
A. Rahman, W. Wiranto, and A. Doewes, “Online News Classification Using Multinomial Naive Bayes,†ITSMART J. Teknol. dan Inf., vol. 6, no. 1, pp. 32–38, 2017.
F. Handayani and S. Pribadi, “Implementasi Algoritma Naive Bayes Classifier dalam Pengklasifikasian Teks Otomatis Pengaduan dan Pelaporan Masyarakat melalui Layanan Call Center 110,†J. Tek. Elektro, vol. 7, no. 1, pp. 19–24, 2015, doi: 10.15294/jte.v7i1.8585.
N. Saputra, T. B. Adji, and A. E. Permanasari, “Analisis Sentimen Data Presiden Jokowi dengan Preprocessing Normalisasi dan Stemming Menggunakan Metode Naive Bayes dan SVM,†J. Din. Inform., vol. 5, no. November, p. 12, 2015.
A. H. Setianingrum, D. H. Kalokasari, and I. M. Shofi, “Implementasi Algoritma Multinomial Naive Bayes Classifier,†J. Tek. Inform., vol. 10, no. 2, pp. 109–118, 2018, doi: 10.15408/jti.v10i2.6822.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
