Naive Bayes Algorithm to Enhance Sentiment Analysis of Coursera Application Reviews on Google Play Store

Masdarul Rizqi; Martanto; Arif Rinaldi Dikanda; Dede Rohman

doi:10.59934/jaiea.v4i2.758

Authors

Masdarul Rizqi STMIK IKMI Cirebon
Martanto STMIK IKMI Cirebon
Arif Rinaldi Dikanda STMIK IKMI Cirebon
Dede Rohman STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.758

Keywords:

Sentiment Analysis, Naive Bayes, Coursera, User Reviews, Google Play Store

Abstract

Coursera is an online learning platform that provides various courses and certifications. This study aims to analyze user perceptions of the Coursera application after the reviews are translated into Indonesian, identify factors that influence positive and negative sentiment, and activate the effectiveness of the Naive Bayes algorithm in classifying review sentiment. The method used is Knowledge Discovery in Databases (KDD), with stages of data collection, preprocessing, and sentiment analysis using Naive Bayes. The results of the study show that the translation of reviews does not change the essence of user perception. Analysis of key words reveals positive experiences such as "kursus", "berguna", and "terima kasih", as well as criticism related to application performance. Factors such as price, content, and user experience play an important role in positive sentiment, while technical issues are the main cause of negative sentiment. The Naive Bayes model shows high accuracy with an accuracy value of 83.62%, precision of 83.34%, recall of 87%, and F1-score of 85.2%. These results indicate that the Naive Bayes algorithm is effective in analyzing sentiment of Coursera application user reviews. Further research is recommended to explore other algorithms or expand the analysis by considering additional factors that can influence user sentiment

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References

S. Styawati, A. R. Isnain, N. Hendrastuty, and L. Andraini, “Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis,” J. Inform. J. Pengemb. IT, vol. 6, no. 1, pp. 56–60, 2021, doi: 10.30591/jpit.v6i1.3245.

T. Krisdiyanto and E. M. O. Nurharyanto, “Analisis Sentimen Opini Masyarakat Indonesia Terhadap Kebijakan PPKM pada Media Sosial Twitter Menggunakan Naïve Bayes Clasifiers,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 7, no. 1, pp. 32–37, 2021, doi: 10.24014/coreit.v7i1.12945.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 697–711, 2021, doi: 10.30645/j-sakti.v5i2.369.

M. Hudha, E. Supriyati, and T. Listyorini, “Analisis Sentimen Pengguna Youtube Terhadap Tayangan #Matanajwamenantiterawan Dengan Metode Naïve Bayes Classifier,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 1–6, 2022, doi: 10.33387/jiko.v5i1.3376.

A. Muzaki and A. Witanti, “Sentimen Analisis Masyarakat Di Twitter Terhadap Pilkada 2020 Ditengah Pandemic Covid-19 Dengan Metode NaïVe Bayes Classifier,” J. Tek. Inform., vol. 2, no. 2, pp. 101–107, 2021, doi: 10.20884/1.jutif.2021.2.2.51.

Friska Aditia Indriyani, Ahmad Fauzi, and Sutan Faisal, “Analisis sentimen aplikasi tiktok menggunakan algoritma naïve bayes dan support vector machine,” TEKNOSAINS J. Sains, Teknol. dan Inform., vol. 10, no. 2, pp. 176–184, 2023, doi: 10.37373/tekno.v10i2.419.

Styawati, N. Hendrastuty, and A. R. Isnain, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 6, no. 3, pp. 150–155, 2021, doi: 10.30591/jpit.v6i3.2870.

F. F. Rachman and S. Pramana, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter,” Heal. Inf. Manag. J., vol. 8, no. 2, pp. 100–109, 2020, doi: 10.47007/inohim.v8i2.223.

H. Hermanto, A. Mustopa, and A. Y. Kuntoro, “Algoritma Klasifikasi Naive Bayes Dan Support Vector Machine Dalam Layanan Komplain Mahasiswa,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 2, pp. 211–220, 2020, doi: 10.33480/jitk.v5i2.1181.

N. P. G. Naraswati, R. Nooraeni, D. C. Rosmilda, D. Desinta, F. Khairi, and R. Damaiyanti, “Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification,” Sistemasi, vol. 10, no. 1, pp. 228–238, 2021, doi: 10.32520/stmsi.v10i1.1179.

N. L. P. M. Putu, Ahmad Zuli Amrullah, and Ismarmiaty, “Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 123–131, 2021, doi: 10.29207/resti.v5i1.2587.

F. Romadoni, Y. Umaidah, and B. N. Sari, “Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 9, no. 2, pp. 247–253, 2020, doi: 10.32736/sisfokom.v9i2.903.