ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI JASA OJEK ONLINE MAXIM PADA GOOGLE PLAY DENGAN METODE NAÏVE BAYES CLASSIFIER
DOI:
https://doi.org/10.23960/jitet.v12i1.3628Abstract Views: 1704 File Views: 1276
Abstract
In this age of digitalization, public transportation has developed, such as the creation of Online Transportation or commonly called Online Ojek. Online transportation is clear evidence of the development of application-based technology. Now with the Online ojek service can provide convenience in ordering ojek. Currently, more and more online ojek service companies are emerging, one of which is Maxim. Maxim has been enjoyed by approximately 47 cities in Indonesia. Maxim is also famous for its relatively affordable price. With the many enthusiasts of the Maxim application, the author is interested in conducting Sentiment Analysis on this application. Sentiment Analysis is done by analyzing user reviews of the Maxim application on the Google Play Store with the Naïve Bayes Classifier method. The author conducts this research, namely to find out the opinions or opinions of users which can later be used as input for application developers so that later the Maxim application can continue to be improved in terms of quality. The results showed that the Maxim application received many positive comments, but there were also some negative comments that could be used as evaluation material for the application developer.Downloads
References
E. Wahyusetyawati, “Dilema pengaturan transportasi online,” Jurnal RechtsVinding. ISSN, pp. 2089–9009, 2017.
F. V. Sari and A. Wibowo, “Analisis Sentimen Pelanggan Toko Online Jd. Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 10, no. 2, pp. 681–686, 2019.
A. Z. Amrullah, A. S. Anas, and M. A. J. Hidayat, “Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square,” Jurnal Bumigora Information Technology (BITe), vol. 2, no. 1, pp. 40–44, 2020.
A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 9, no. 2, pp. 785–795, 2022.
N. A. Rahma, G. Garno, and N. Sulistiyowati, “Analisis Sentimen Tempat Wisata Di Jakarta Pasca Covid-19 Dengan Algoritma Naïve Bayes,” Jurnal Pendidikan dan Konseling (JPDK), vol. 4, no. 6, pp. 5894–5908, 2022.
J. A. Septian, T. M. Fachrudin, and A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor,” INSYST: Journal of Intelligent System and Computation, vol. 1, no. 1, pp. 43–49, 2019.
L. B. Ilmawan and M. A. Mude, “Perbandingan metode klasifikasi Support Vector Machine dan Naïve Bayes untuk analisis sentimen pada ulasan tekstual di Google Play Store,” Ilk. J. Ilm, vol. 12, no. 2, pp. 154–161, 2020.
W. Yulita, “Analisis sentimen terhadap opini masyarakat tentang vaksin covid-19 menggunakan algoritma naïve bayes classifier,” Jurnal Data Mining dan Sistem Informasi, vol. 2, no. 2, pp. 1–9, 2021.
M. R. A. Nasution and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,” J. Inform, vol. 6, no. 2, pp. 226–235, 2019.
F. F. Irfani, M. Triyanto, and A. D. Hartanto, “Analisis Sentimen Review Aplikasi Ruangguru Menggunakan Algoritma Support Vector Machine,” JBMI (Jurnal Bisnis, Manajemen, dan Inform., vol. 16, no. 3, p. 258, 2020, doi: 10.26487/jbmi. v16i3. 8607, 2020.
D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional,” Jurnal Tekno Kompak, vol. 15, no. 1, pp. 131–145, 2021.
S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, “Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19),” Jurnal Media Informatika Budidarma, vol. 5, no. 2, pp. 406–414, 2021.
N. Fitriyah, B. Warsito, and I. M. Di Asih, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM),” Jurnal Gaussian, vol. 9, no. 3, pp. 376–390, 2020.
A. Santosa, I. Purnamasari, and R. Mayasari, “Pengaruh Stopword Removal dan Stemming Terhadap Performa Klasifikasi Teks Komentar Kebijakan New Normal Menggunakan Algoritma LSTM,” J-SAKTI (Jurnal Sains Komputer dan Informatika), vol. 6, no. 1, pp. 81–93, 2022.
