Analisis Sentimen Ulasan Aplikasi Binar Pada Google Play Store Menggunakan Algoritma Naïve Bayes
Abstract
Binar is an online learning platform that provides courses and certifications in the digital field. The Binar app has been downloaded 500,000 times and has a rating of 3.6 on the Google Play Store. However, user ratings sometimes do not match their reviews. In application development, not only the number of ratings but also user opinions need to be considered. Therefore, developers must be able to interpret every opinion given, and sentiment analysis was conducted using the Naïve Bayes Multinomial and Bernoulli algorithms along with Information Gain feature selection to interpret user opinions. This study used the Knowledge Discovery in Database (KDD) method. The data used consisted of 713 reviews of the Binar app, including 518 positive and 195 negative reviews. The best results were obtained in the 9:1 data split scenario with the Bernoulli Naïve Bayes model achieving an accuracy of 93.06%, precision of 87.04%, recall of 100%, f1-score of 93.07%, and AUC of 0.988.
References
Aini, S. H. A., Sari, Y. A., & Arwan, A. (2018). Feature selection Information Gain untuk Klasifikasi Penyakit Jantung Menggunakan Kombinasi Metode K-Nearest Neighbor dan Naïve Bayes. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(9), 2546–2554. http://j-ptiik.ub.ac.id
Chingmuankim, N. (2022). A comparative study of Naïve Bayes Classifiers with improved technique on Text Classification. TechRxiv,1–8. https://doi.org/10.36227/techrxiv.19918360.v1
Databoks. (2021). Pengguna Smartphone diperkirakan Mencapai 89% Populasi pada 2025 (15 September 2020). Diakses pada 10 Maret 2022, pukul 13.00 dari https://databoks.katadata.co.id/datapublish/2020/09/15/pengguna-smartphone-diperkirakan-mencapai-89-populasi-pada-2025
Fanissa, S., Fauzi, M. A., & Adinugroho, S. (2018). Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naïve Bayes dan Feature selection Query Expansion Ranking. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(8), 2766–2770. http://j-ptiik.ub.ac.id
Fransiska, S., & Irham Gufroni, A. (2020). Sentiment Analysis Provider by.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method. Scientific Journal of Informatics, 7(2), 2407–7658. http://journal.unnes.ac.id/nju/index.php/sji
Gorunescu, F. (2010). Data Mining concepts, model, and techniques. Chennai, India: Springer. https://doi.org/10.1007/978-3-642-19721-5
Hadianto, N., Novitasari, H. B., & Rahmawati, A. (2019). Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network. Jurnal Pilar Nusa Mandiri, 15(2), 163–170. https://doi.org/10.33480/pilar.v15i2.658
Hasibuan, M. R., & Marji. (2019). Pemilihan Fitur dengan Information Gain untuk Klasifikasi Penyakit Gagal Ginjal menggunakan Metode Modified K-Nearest Neighbor (MKNN). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(11), 10435–10443.
Isnanda, A., Umaidah, Y., & Jaman, J. H. (2021). Implementasi Naïve Bayes Classifier Dan Information Gain Pada Analisis Sentimen Penggunaan E-Wallet Saat Pandemi. Jurnal Teknologi Informatika Dan Komputer, 7(2), 144–153. https://doi.org/10.37012/jtik.v7i2.648
Kulkarni, A., & Shivananda, A. (2019). Natural Language Processing Recipes. In Natural Language Processing Recipes. Bangalore, India:Apress Berkeley, CA. https://doi.org/10.1007/978-1-4842-4267-4
Liu, B. (2012) Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. California, Amerika Serikat:Morgan & Claypool Publishers, 5, 1-167. https://doi.org/10.1007/978-3-031-02145-9
Made, I Adnyana, B. (2019). Penerapan Feature selection untuk Prediksi Lama Studi Mahasiswa. Jurnal Sistem Dan Informatika, 13, 72–76.
Manning, C. D., Raghavan, P., & Schütze, H. (2009). An Introduction to Information Retrieval. England:Cambridge University Press Cambridge, 53(9), 462–463. https://doi.org/10.1108/00242530410565256
Ramos, S., Soares, J., Cembranel, S. S., Tavares, I., Foroozandeh, Z., Vale, Z., & Fernandes, R. (2021). Data Mining techniques for electricity customer characterization. Procedia Computer Science, 186, 475–488. https://doi.org/10.1016/j.procs.2021.04.168
Reza Satria, A., & Adinugroho, S. (2020). Analisis Sentimen Ulasan Aplikasi Mobile menggunakan Algoritma Gabungan Naïve Bayes dan C4.5 berbasis Normalisasi Kata Levenshtein Distance. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 4(11), 4154–4163. http://j-ptiik.ub.ac.id
Salsabila, S. M., Alim Murtopo, A., & Fadhilah, N. (2022). Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier. Jurnal Minfo Polgan, 11(2), 30–35. https://doi.org/10.33395/jmp.v11i2.11640
Saputra, S. A., Rosiyadi, D., Gata, W., & Husain, S. M. (2019). Google Play E-Wallet Sentiment Analysis Using Naïve Bayes Algorithm Based on Particle Swarm Optimization. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 377–382.
Sari, A. E., Widowati, S., & Lhaksmana, K. M. (2019). Klasifikasi Ulasan Pengguna Aplikasi Mandiri Online di Google Play Store dengan Menggunakan Metode Information Gain dan Naïve Bayes Classifier. E-Proceeding of Engineering, 6(2), 9143–9157.
Singh, G., Kumar, B., Gaur, L., & Tyagi, A. (2019). Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification. International Conference on Automation, Computational and Technology Management, ICACTM, April, 593–596. https://doi.org/10.1109/ICACTM.2019.8776800
Verdikha, N. A., Adji, T. B., & Permanasari, A. E. (2018). Komparasi Metode Oversampling Untuk Klasifikasi Teks Ujaran Kebencian. Seminar Nasional Teknologi Informasi Dan Multimedia 2018, 85–90.
Wardani, N. S., Prahutama, A., & Kartikasari, P. (2020). Analisis Sentimen Pemindahan Ibu Kota Negara Dengan Klasifikasi Naïve Bayes Untuk Model Bernoulli Dan Multinomial. Jurnal Gaussian, 9(3), 237–246. https://doi.org/10.14710/j.gauss.v9i3.27963.
Chingmuankim, N. (2022). A comparative study of Naïve Bayes Classifiers with improved technique on Text Classification. TechRxiv,1–8. https://doi.org/10.36227/techrxiv.19918360.v1
Databoks. (2021). Pengguna Smartphone diperkirakan Mencapai 89% Populasi pada 2025 (15 September 2020). Diakses pada 10 Maret 2022, pukul 13.00 dari https://databoks.katadata.co.id/datapublish/2020/09/15/pengguna-smartphone-diperkirakan-mencapai-89-populasi-pada-2025
Fanissa, S., Fauzi, M. A., & Adinugroho, S. (2018). Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naïve Bayes dan Feature selection Query Expansion Ranking. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(8), 2766–2770. http://j-ptiik.ub.ac.id
Fransiska, S., & Irham Gufroni, A. (2020). Sentiment Analysis Provider by.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method. Scientific Journal of Informatics, 7(2), 2407–7658. http://journal.unnes.ac.id/nju/index.php/sji
Gorunescu, F. (2010). Data Mining concepts, model, and techniques. Chennai, India: Springer. https://doi.org/10.1007/978-3-642-19721-5
Hadianto, N., Novitasari, H. B., & Rahmawati, A. (2019). Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network. Jurnal Pilar Nusa Mandiri, 15(2), 163–170. https://doi.org/10.33480/pilar.v15i2.658
Hasibuan, M. R., & Marji. (2019). Pemilihan Fitur dengan Information Gain untuk Klasifikasi Penyakit Gagal Ginjal menggunakan Metode Modified K-Nearest Neighbor (MKNN). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(11), 10435–10443.
Isnanda, A., Umaidah, Y., & Jaman, J. H. (2021). Implementasi Naïve Bayes Classifier Dan Information Gain Pada Analisis Sentimen Penggunaan E-Wallet Saat Pandemi. Jurnal Teknologi Informatika Dan Komputer, 7(2), 144–153. https://doi.org/10.37012/jtik.v7i2.648
Kulkarni, A., & Shivananda, A. (2019). Natural Language Processing Recipes. In Natural Language Processing Recipes. Bangalore, India:Apress Berkeley, CA. https://doi.org/10.1007/978-1-4842-4267-4
Liu, B. (2012) Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. California, Amerika Serikat:Morgan & Claypool Publishers, 5, 1-167. https://doi.org/10.1007/978-3-031-02145-9
Made, I Adnyana, B. (2019). Penerapan Feature selection untuk Prediksi Lama Studi Mahasiswa. Jurnal Sistem Dan Informatika, 13, 72–76.
Manning, C. D., Raghavan, P., & Schütze, H. (2009). An Introduction to Information Retrieval. England:Cambridge University Press Cambridge, 53(9), 462–463. https://doi.org/10.1108/00242530410565256
Ramos, S., Soares, J., Cembranel, S. S., Tavares, I., Foroozandeh, Z., Vale, Z., & Fernandes, R. (2021). Data Mining techniques for electricity customer characterization. Procedia Computer Science, 186, 475–488. https://doi.org/10.1016/j.procs.2021.04.168
Reza Satria, A., & Adinugroho, S. (2020). Analisis Sentimen Ulasan Aplikasi Mobile menggunakan Algoritma Gabungan Naïve Bayes dan C4.5 berbasis Normalisasi Kata Levenshtein Distance. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 4(11), 4154–4163. http://j-ptiik.ub.ac.id
Salsabila, S. M., Alim Murtopo, A., & Fadhilah, N. (2022). Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier. Jurnal Minfo Polgan, 11(2), 30–35. https://doi.org/10.33395/jmp.v11i2.11640
Saputra, S. A., Rosiyadi, D., Gata, W., & Husain, S. M. (2019). Google Play E-Wallet Sentiment Analysis Using Naïve Bayes Algorithm Based on Particle Swarm Optimization. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 377–382.
Sari, A. E., Widowati, S., & Lhaksmana, K. M. (2019). Klasifikasi Ulasan Pengguna Aplikasi Mandiri Online di Google Play Store dengan Menggunakan Metode Information Gain dan Naïve Bayes Classifier. E-Proceeding of Engineering, 6(2), 9143–9157.
Singh, G., Kumar, B., Gaur, L., & Tyagi, A. (2019). Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification. International Conference on Automation, Computational and Technology Management, ICACTM, April, 593–596. https://doi.org/10.1109/ICACTM.2019.8776800
Verdikha, N. A., Adji, T. B., & Permanasari, A. E. (2018). Komparasi Metode Oversampling Untuk Klasifikasi Teks Ujaran Kebencian. Seminar Nasional Teknologi Informasi Dan Multimedia 2018, 85–90.
Wardani, N. S., Prahutama, A., & Kartikasari, P. (2020). Analisis Sentimen Pemindahan Ibu Kota Negara Dengan Klasifikasi Naïve Bayes Untuk Model Bernoulli Dan Multinomial. Jurnal Gaussian, 9(3), 237–246. https://doi.org/10.14710/j.gauss.v9i3.27963.
Published
2023-06-10
Section
Articles
