Analisis Sentimen Review Aplikasi Media Berita Online Pada Google Play menggunakan Metode Algoritma Support Vector Machines (SVM) Dan Naive Bayes
Online news media has become one of the most frequently consumed mass media by the public, which is able to beat the previous generation of media such as electronic media and print media. The advantage of online media compared to print media in general lies in its up-to-date, real-time and practical nature. In order to continue to maintain and improve the performance of online news media, public assessment of the services and news presented is very important. The public rating can be seen from the Google Play site in the user review column. Sentiment analysis can be used to analyze these reviews by classifying positive and negative reviews. The user review data was then labeled and analyzed using the Naïve Bayes algorithm and Support Vector Machines. The experimental results show that the accuracy of machine learning for sentiment analysis on online news media reviews Google Play reaches 90% and the deep learning approach outperforms Support Vector Machine (88%) while Naïve Bayes (87%).
B. Bayquni, “Partisipasi khalayak media online terhadap liptan6. com dalam memenangkan persaingan industri media massa di Indonesia,” Jurnal Pustaka Komunikasi, vol. 1, no. 2, pp. 228–237, 2018.
S. Yunus, Jurnalistik terapan. Bogor: Ghalia Indonesia, 2010.
A. Mustopa, E. B. Pratama, A. Hendini, and D. Risdiansyah, “Analysis of user reviews for the pedulilindungi application on google play using the support vector machine and naive bayes algorithm based on particle swarm optimization,” in 2020 Fifth International Conference on Informatics and Computing (ICIC), 2020, pp. 1–7.
H. Hermanto, A. Y. Kuntoro, T. Asra, N. Nurajijah, L. Effendi, and R. Ocanitra, “Sentiment Analysis On Gojek And Grab User Reviews Using Svm Algorithm Based On Particle Swarm Optimization,” Jurnal Pilar Nusa Mandiri, vol. 16, no. 1, Art. no. 1, Mar. 2020, doi: 10.33480/pilar.v16i1.1304.
T. M. Ma, K. Yamamori, and A. Thida, “A comparative approach to Naïve Bayes classifier and support vector machine for email spam classification,” in 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), 2020, pp. 324–326.
M. M. J. Soumik, S. S. M. Farhavi, F. Eva, T. Sinha, and M. S. Alam, “Employing machine learning techniques on sentiment analysis of google play store bangla reviews,” in 2019 22nd International Conference on Computer and Information Technology (ICCIT), 2019, pp. 1–5.
S. Santoso, “Analisis Resepsi Audiens Terhadap Berita Kasus Meiliana di Media Online,” Komuniti: Jurnal Komunikasi dan Teknologi Informasi, vol. 12, no. 2, pp. 140–154, 2021.
Adiwijaya, “Text Mining dan Knowledge Discovery,” 2006.
C. Darujati and A. B. Gumelar, “Pemanfaatan teknik supervised untuk klasifikasi teks bahasa indonesia,” Jurnal Bandung Text Mining, vol. 16, no. 1, pp. 5–1, 2012.
F. Handayani and F. S. Pribadi, “Implementasi algoritma naive bayes classifier dalam pengklasifikasian teks otomatis pengaduan dan pelaporan masyarakat melalui layanan call center 110,” Jurnal Teknik Elektro, vol. 7, no. 1, pp. 19–24, 2015.
A. A. Muin, “Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi),” Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar, vol. 2, no. 1, pp. 22–26, 2016.
M. Awad and R. Khanna, “Support vector machines for classification,” in Efficient Learning Machines, Springer, 2015, pp. 39–66.
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