Analisis Sentimen Terhadap Kepuasan Layanan Tiket.com Menggunakan Metode Naïve Bayes

Authors

  • Alfia Nur Fadhila Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
  • Khaira Nadhlif Aulia Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
  • Dea Halmia Febianti Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
  • Darin Marwa Fadiyah Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
  • Fakhris Khusnu Reza Mahfud Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia

DOI:

https://doi.org/10.21580/wjit.2024.6.1.16691

Keywords:

Sentiment analysis, meta data, Naïve Bayes

Abstract

One of the transportation booking service applications that are widely downloaded by the people of Indonesia is tiket.com.  tiket.com application occupies the second rank as a platform that provides user needs in the field of services with total downloads in the Play Store reaching 10 million users with 257 thousand reviews submitted by tiket.com users. The purpose of this study was to analyze sentimental user satisfaction in using tiket.com application using the Naïve Bayes method. The results of this study are written in the form of summaries and tables so as to get the results that customers are quite satisfied with the services provided by the tiket.com application. And it can be concluded where the Accuracy, Precision and Recall data that get values above 50% so that it can be said that users are quite satisfied with the service of tiket.com application.

Downloads

Download data is not yet available.

References

Amrullah, A. Z., Anas, A. S., & Hidayat, M. A. J. (2020). Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square.

Apriani, R., & Gustian, D. (2019). ANALISIS SENTIMEN DENGAN NAÏVE BAYES TERHADAP KOMENTAR APLIKASI TOKOPEDIA. Jurnal Rekayasa Teknologi Nusa Putra, 6(1), Article 1. https://doi.org/10.52005/rekayasa.v6i1.86

Asosiasi Penyelenggara Jasa Internet Indonesia. (n.d.). Retrieved May 29, 2023, from https://apjii.or.id/berita/d/survei-apjii-pengguna-internet-di-indonesia-tembus-215-juta-orang

Dimas, D. (2023). Sederet Aplikasi Travel Terbanyak Diunduh di Indonesia pada 2022. Dataindonesia.id. https://dataindonesia.id/ragam/detail/sederet-aplikasi-travel-terbanyak-diunduh-di-indonesia-pada-2022

Febriyani, E., & Februariyanti, H. (2023). Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naive Bayes Classifier Di Twitter. Jurnal Tekno Kompak, 17(1), Article 1. https://doi.org/10.33365/jtk.v17i1.2061

Nindy, R. A. (2018). Pengaruh Kemudahan Akses dan Diskon Terhadap Keputusan Pembelian Pada Situs Online Tiket.com (Studi Kasus pada Mahasiswa Aktif S1 Universitas Diponegoro) [Other, Faculty of Social and Political Sciences]. http://eprints.undip.ac.id/75325/

Ratnawati, F. (2018). Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter. INOVTEK Polbeng - Seri Informatika, 3(1), Article 1. https://doi.org/10.35314/isi.v3i1.335

Retnoningsih, E., & Pramudita, R. (2020). Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python. BINA INSANI ICT JOURNAL, 7(2), 156. https://doi.org/10.51211/biict.v7i2.1422

Samsir, S., Ambiyar, A., Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(1), 157. https://doi.org/10.30865/mib.v5i1.2580

Khasanah, N., Mustofa, H., Mahfudh, A. A., & Bakhri, S. (2023). Pendampingan Pengelolaan Jurnal Online di Universitas Selamat Sri (UNISS) Kendal. Jurnal Pengabdian KOLABORATIF, 1(1), 1-12. https://doi.org/10.26623/jpk.v1i1.6053

Setyo Legianto, 14523195. (2019a). IMPLEMENTASI TEXT MINING UNTUK MENDETEKSI HATE SPEECH PADA TWITTER [Thesis, Universitas Islam Indonesia]. https://dspace.uii.ac.id/handle/123456789/17254

Setyo Legianto, 14523195. (2019b). IMPLEMENTASI TEXT MINING UNTUK MENDETEKSI HATE SPEECH PADA TWITTER [Thesis, Universitas Islam Indonesia]. https://dspace.uii.ac.id/handle/123456789/17254

Syah, H., & Witanti, A. (2022). ANALISIS SENTIMEN MASYARAKAT TERHADAP VAKSINASI COVID-19 PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Jurnal Sistem Informasi dan Informatika (Simika), 5(1), 59–67. https://doi.org/10.47080/simika.v5i1.1411

Downloads

Published

2024-04-20

How to Cite

[1]
A. N. Fadhila, K. N. Aulia, D. H. Febianti, D. M. Fadiyah, and F. K. R. Mahfud, “Analisis Sentimen Terhadap Kepuasan Layanan Tiket.com Menggunakan Metode Naïve Bayes”, Walisongo J. Inf. Technol., vol. 6, no. 1, pp. 1–8, Apr. 2024.

Issue

Vol. 6 No. 1 (2024): Walisongo Journal of Information Technology

Section

Articles

License

Copyright (c) 2024 Walisongo Journal of Information Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The copyright of the received article shall be assigned to the journal as the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to the published articles. Authors are allowed to use their articles for any legal purposes deemed necessary without written permission from the journal with an acknowledgment of initial publication to this journal.


Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.