SENTIMENT ANALYSIS OF ONLINE GOJEK TRANSPORTATION SERVICES ON TWITTER USING THE NAÏVE BAYES METHOD

  • Muhammad Fahmi (1) Universitas Nusa Mandiri
  • Yuyun Yuningsih (2) Universitas Nusa Mandiri
  • Ari Puspita (3*) Universitas Bina Sarana Informatika

  • (*) Corresponding Author
Keywords: Online Transportation, appraisal analysis, Twitter, Naïve Bayes.

Abstract

Abstract Social media is the most accessed internet content by internet users in Indonesia. This is not surprising, given the many benefits that social media provides, one of which is the benefit of self-expression. Self-expression can include many things, including emotional openness, which is the openness of a person in conveying the emotions he is feeling. Along with the development of social media, emotional disclosure is ubiquitous in social media, one of which is social media Twitter. With the development of information technology, means of transportation are also developing with the existence of online transportation services. Currently, the use of online transportation services has become a necessity, so it is necessary to conduct a sentiment analysis on online transportation services to find out how the public responds to these online transportation services. The purpose of this study is to analyze community responses by analyzing data in the form of tweets and then classifying them into positive, negative, and neutral classes using the Naïve Bayes method because the error rate obtained is lower when the dataset is large, besides that the accuracy of Naive Bayes and the speed is higher. high when applied to a larger dataset. The results of this study indicate that the neutral sentiment level of public tweets is greater than the level of positive sentiment and negative sentiment with an accuracy of 25.00%.

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References

Abdullah and B. A. Saebani, Metode penelitian ekonomi islam : Muamalah. Bandung: Pustaka setia, 2014.

I. Verawati and B. S. Audit, “Algoritma Naïve Bayes Classifier Untuk Analisis Sentiment Pengguna Twitter Terhadap Provider By.u,” J. Media Inform. Budidarma, vol. 6, no. 3, p. 1411, 2022.

Hartanto, “Hartanto 2017 text mining dan sentimen analisis twitter,” J. Psikol. Ilm., vol. 9, no. 1, pp. 18–25, 2017.

A. Wandani, “Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 651–665, 2021.

S. Fransiska and Yolanda, “Analisis Sentimen Twitter Untuk Review Film Menggunakan Algoritma Naive Bayes Classifier ( Nbc ) Pada Sentimen R Programming,” J. Siliwangi, vol. 5, no. 2, pp. 68–71, 2019.

S. Sumanto and L. S. Marita, “Fuzzy Simple Additive Weighting (Fsaw) Untuk Analisa Kelayakan Pemberian Kredit Mobil,” J I M P - J. Inform. Merdeka Pasuruan, vol. 2, no. 2, pp. 1–13, 2017.

E. Dwianto and M. Sadikin, “Analisis Sentimen Transportasi Online pada Twitter Menggunakan Metode Klasifikasi Naïve Bayes dan Support Vector Machine,” Format J. Ilm. Tek. Inform., vol. 10, no. 1, p. 94, 2021.

W. Supriyanti, Kusrini, and A. Amborowati, “Perbandingan Kinerja Algoritma C4.5 Dan Naive Bayes untuk Ketepatan Pemilihan Konsentrasi Mahasiswa,” J. Inf. Politek. Indonusa, vol. 1, no. 3, pp. 61–67, 2016.

T. T. Widowati and M. Sadikin, “Analisis Sentimen Twitter terhadap Tokoh Publik dengan Algoritma Naive Bayes dan Support Vector Machine,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 2, pp. 626–636, 2021.

E. Y. Hidayat, R. W. Hardiansyah, and A. Affandy, “Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network,” J. Nas. Teknol. dan Sist. Inf., vol. 7, no. 2, pp. 108–118, 2021.

M. W. A. Putra, Susanti, Erlin, and Herwin, “Analisis Sentimen Dompet Elektronik Pada Twitter Menggunakan Metode Naïve Bayes Classifier,” IT J. Res. Dev., vol. 5, no. 1, pp. 72–86, 2020.

E. Sumartono and Y. Astria, “Strategi Nafkah Petani Sawit Di Desa Penarik Kecamatan Penarik Kabupaten Mukomuko,” MAHATANI J. Agribisnis (agribus. Agric. Econ. Journal), vol. 1, no. 2, pp. 80–95, 2019.

V. Kevin, S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization ( Online Transportation Sentiment Analysis Using Support Vector Machine Based on Particle Swarm Optimization ),” vol. 9, no. 2, pp. 162–170, 2020.

Bustami, “Penerapan Algoritma Naive Bayes,” J. Inform., vol. 8, no. 1, pp. 884–898, 2014.

K. V. S. Toy, Y. A. Sari, and I. Cholissodin, “Analisis Sentimen Twitter menggunakan Metode Naive Bayes dengan Relevance Frequency Feature Selection (Studi Kasus: Opini Masyarakat mengenai Kebijakan New Normal),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 11, pp. 5068–5074, 2021.

Published
2023-01-31
How to Cite
[1]
M. Fahmi, Y. Yuningsih, and A. Puspita, “SENTIMENT ANALYSIS OF ONLINE GOJEK TRANSPORTATION SERVICES ON TWITTER USING THE NAÏVE BAYES METHOD”, jitk, vol. 8, no. 2, pp. 90 - 96, Jan. 2023.
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