Klasifikasi Opini Masyarakat terhadap Jasa Ekspedisi J&T Express pada Media Sosial Twitter dengan Naïve Bayes

Authors

Keywords:

J&T Express, Naïve Bayes, Sentiment analysis, Classification

Abstract

With the rise of online sales transactions through e-commerce and social media, the impact of changing consumer behavior. This has resulted in many sentiment assessments from users of these expedition services. Expedition services are now one of the most popular services. One of the shipping service companies operating in Indonesia is J&T Express. Through social media, especially Twitter with the number of followers up to 154,439 and the number of tweets up to 103,100, a user can form an opinion on the performance of J&T Express and get data as many as 1694 tweets. Machine learning algorithms are needed to enable sentiment analysis to be classified. One of them is the Naive Bayes algorithm. Before running the classification process, a preprocessing process is needed so that the dataset can be recognized by the system. Based on the tests conducted, the results obtained accurasy of 84%, precision of 76%, and recall of 87%. The results of this research show that the data can be used as a basis for evaluating business decisions.

References

Diamantini, C., Mircoli, A., Potena, D., & Storti, E. (2019). Social information discovery enhanced by sentiment analysis techniques. Future Generation Computer Systems, 95, 816–828. https://doi.org/10.1016/j.future.2018.01.051

Handayani, E. T., & Sulistiyawati, A. (2021). Analisis Sentimen Respon Masyarakat Terhadap Kabar Harian Covid-19 Pada Twitter Kementerian Kesehatan Dengan Metode Klasifikasi Naive Bayes. Jurnal Teknologi Dan Sistem Informasi (JTSI), 2(3), 32–37. http://jim.teknokrat.ac.id/index.php/JTSI

Handoko, W. T., Supriyanto, E., Purwadi, D. I., Budiarso, Z., & Listiyono, H. (2022). Klasifikasi Opini Pengguna Media Sosial Twitter Terhadap JNT Di Indonesia dengan Algoritma Decision Tree. Jurnal Sains Komputer & Informatika (J-SAKTI), 6(2), 790–799.

Ika, N., Kalingara, P., Pratiwi, O. N., & Anggana, H. D. (2021). Analisis Sentimen Review Customer Terhadap Layanan Ekspedisi Jne Dan J & T Express Menggunakan Metode Naïve Bayes Sentiment Analysis Review Customer of Jne and J & T Express Expedition Services Using Naïve Bayes Method. E-Proceeding of Engineering, 8(5), 9035–9048.

Irawan, F. R., Jazuli, A., Khotimah, T., Studi, P., Informatika, T., Kudus, U. M., & Neighbor, K. (2022). Analisis Sentimen terhadap Pengguna Gojek Menggunakan Metode K-Nearset Neighbors. JIKO (Jurnal Informatika Dan Komputer), 5(1), 62–68. https://doi.org/10.33387/jiko

Lesmana, R., & Andarsyah, R. (2022). Model Klasifikasi Multinomial Naïve Bayes Untuk Analisis Sentiment Terkait Non-Fungible Token. Jurnal Teknik Informatika, 14(3), 135–139.

Nurul Hidayah, Y., & Sahibu, S. (2021). Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 820–826. https://doi.org/10.29207/resti.v5i4.3146

Puh, K., & Bagic Babac, M. (2022). Predicting sentiment and rating of tourist reviews using machine learning. Journal of Hospitality and Tourism Insights, 6(3), 1188–1204. https://doi.org/10.1108/JHTI-02-2022-0078

Ruz, G. A., Henríquez, P. A., & Mascareño, A. (2020). Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106, 92–104. https://doi.org/10.1016/j.future.2020.01.005

Singh, G., Kumar, B., Gaur, L., & Tyagi, A. (2019). Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification. 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019, 593–596. https://doi.org/10.1109/ICACTM.2019.8776800

Verawati, I., & Audit, B. S. (2022). Algoritma Naïve Bayes Classifier Untuk Analisis Sentiment Pengguna Twitter Terhadap Provider By . u. 6, 1411–1417. https://doi.org/10.30865/mib.v6i3.4132

Yutika, C. H., & Faraby, S. Al. (2021). Analisis Sentimen Berbasis Aspek pada Review Female Daily Menggunakan TF-IDF dan Naïve Bayes. 5(April), 422–430. https://doi.org/10.30865/mib.v5i2.2845

Published

2023-07-30

How to Cite

Ansyahry, B. R., & Al Amin, I. H. (2023). Klasifikasi Opini Masyarakat terhadap Jasa Ekspedisi J&T Express pada Media Sosial Twitter dengan Naïve Bayes. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 6(3), 402–407. Retrieved from https://openjournal.unpam.ac.id/index.php/JTSI/article/view/30878