Sentiment Analysis of pegipegi.com Review on Google Play Store with Naïve Bayes

Muhamad Naufal Burhanuddin Balit, Fandy Setyo Utomo

Abstract


In the current era, a shift in consumer behavior is evident in the use of online platforms for booking tickets, involving various services such as flights, hotels, trains, buses, and entertainment. PegiPegi.com, as a rapidly growing online travel agent in Indonesia, demonstrates success by understanding the value of technology and maintaining strong partnerships. This phenomenon also impacts sentiment analysis, where users of this platform often provide reviews. This research aims to apply the Naïve Bayes classification method in sentiment analysis of PegiPegi.com reviews, focusing on understanding customer satisfaction and service improvement. By combining these approaches, the study contributes to a deeper understanding of user responses to OTA services and presents the evaluation results of the Multinomial Naive Bayes classification model with an accuracy rate of 89.5%. The high precision in the Negative class indicates the model's ability to identify negative reviews. However, there are challenges in classifying the Neutral class, suggesting potential for further improvement. Nevertheless, the F1-score of 0.522 reflects a good balance between overall precision and recall.

Full Text:

PDF

References


A. B. P. Negara, H. Muhardi, and I. M. Putri, “Analisis Sentimen Maskapai Penerbangan menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, p. 599, 2020, doi: 10.25126/jtiik.2020711947.

I. A. Wulandari, F. Umar, and E. Setyawati, “Strategi Peningkatan Kualitas Pelayan Online Travel Agent Pegipegi dengan Quality Function Deployment,” J. REKAYASA dan OPTIMASI Sist. Ind., vol. 2, no. October, pp. 32–37, 2020, [Online]. Available: https://journal.univpancasila.ac.id/index.php/jrosi/article/view/2474

Fiaz, “Strategi Bisnis Pegipegi: Menghadirkan Kemudahan dalam Perjalanan Anda,” https://perpusteknik.com/. [Online]. Available: https://perpusteknik.com/strategi-bisnis-pegipegi/

P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.

M. Arsyam and M. Yusuf Tahir, “Ragam Jenis Penelitian dan Perspektif,” Al-Ubudiyah J. Pendidik. dan Stud. Islam, vol. 2, no. 1, pp. 37–47, 2021, doi: 10.55623/au.v2i1.17.

I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen terhadap Penggunaan Aplikasi Shopee mengunakan Algoritma Support Vector Machine (SVM),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.

S. Informasi, U. Darwan Ali, J. Batu Berlian, and K. Tengah, “Penerapan Metode Naive Bayes dalam Mengklasifikasi Penerima BLT pada Desa Pelangsian,” J. JUPITER, vol. 14, no. 2, pp. 64–70, 2022.

R. Apriani et al., “Analisis Sentimen dengan Naïve Bayes terhadap Komentar Aplikasi Tokopedia,” J. Rekayasa Teknol. Nusa Putra, vol. 6, no. 1, pp. 54–62, 2019, [Online]. Available: https://rekayasa.nusaputra.ac.id/article/view/86

A. A. A. Sumanjaya, Indriati, and A. Ridok, “Analisis Sentimen Data Tweets terhadap Penanganan Covid-19 di Indonesia menggunakan Metode Naïve Bayes dan Pemilihan Kata Bersentimen menggunakan Lexicon Based,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 4, pp. 1865–1872, 2022, [Online]. Available: http://j-ptiik.ub.ac.id

O. P. Zusrotun, A. C. Murti, and R. Fiati, “Analisis Sentimen terhadap Belajar Online pada Media Sosial Twitter menggunakan Algoritma Naive Bayes,” J. Nas. Pendidik. Tek. Inform., vol. 11, no. 3, pp. 310–319, 2022, doi: 10.23887/janapati.v11i3.49160.

K. M. Elistiana, B. A. Kusuma, P. Subarkah, and H. A. A. Rozaq, “Improvement of Naive Bayes Algorithm in Sentiment Analysis of Shopee Application Reviews on Google Play Store,” vol. 4, no. 6, pp. 1431–1436, 2023, [Online]. Available: https://doi.org/10.52436/1.jutif.2023.4.6.1486

W. Khofifah, D. N. Rahayu, and A. M. Yusuf, “Analisis Sentimen menggunakan Naive Bayes untuk Melihat Review Masyarakat terhadap Tempat Wisata Pantai di Kabupaten Karawang pada Ulasan Google Maps,” J. Interkom J. Publ. Ilm. Bid. Teknol. Inf. dan Komun., vol. 16, no. 4, pp. 28–38, 2022, doi: 10.35969/interkom.v16i4.192.

J. Dongga, A. ` Sarungallo, N. Koru, and G. Lante, “Implementasi Data Mining menggunakan Algoritma Apriori dalam Menentukan Persediaan Barang (Studi Kasus: Toko Swapen Jaya Manokwari),” G-Tech J. Teknol. Terap., vol. 7, no. 1, pp. 119–126, 2023, doi: 10.33379/gtech.v7i1.1938.

N. Noviyanto, “Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 berdasarkan Negara di Benua Asia,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 183–188, 2020, doi: 10.31294/p.v22i2.8808.

F. Alghifari and D. Juardi, “Penerapan Data Mining pada Penjualan Makanan dan Minuman menggunakan Metode Algoritma Naïve Bayes,” J. Ilm. Inform., vol. 9, no. 02, pp. 75–81, 2021, doi: 10.33884/jif.v9i02.3755.

A. Z. Rizquina and C. I. Ratnasari, “Implementasi Web Scraping untuk Pengambilan Data pada Website E-Commerce,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 4, pp. 377–383, 2023, doi: 10.47233/jteksis.v5i4.913.

Amazon, “Apa Itu Pelabelan Data?,” https://aws.amazon.com/. Accessed: Jan. 30, 2024. [Online]. Available: https://aws.amazon.com/id/what-is/data-labeling/




DOI: https://doi.org/10.32520/stmsi.v13i3.3913

Article Metrics

Abstract view : 177 times
PDF - 97 times

Refbacks

  • There are currently no refbacks.


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