Metode Seleksi Fitur Untuk Klasifikasi Sentimen Menggunakan Algoritma Naive Bayes: Sebuah Literature Review
DOI:
https://doi.org/10.30865/mib.v5i3.2983Keywords:
Sentiment Analysis, Social Media, Feature Selection Method, Naive Bayes Algorithm, Systematic Literature ReviewAbstract
In the era of the industrial revolution 4.0 as it is today, where the internet is a necessity for people to live their daily lives. The high intensity of internet use in the community, it causes the distribution of information in it to spread widely and quickly. The rapid distribution of information on the internet is also in line with the growing growth of digital data, so that the public opinions contained therein become important things. Because, from this digital data, it can be processed with sentiment analysis in order to obtain useful information about issues that are developing in the community or to find out public opinion on a company's product. The number of studies related to sentiment analysis that applies the Naive Bayes algorithm to solve the problem, so researchers are interested in conducting research on the use of feature selection for the algorithm. Therefore, this research was conducted to determine what feature selection is the most optimal when combined with the Naive Bayes algorithm using the Systematic Literature Review (SLR) research method. The results of this study concluded that the most optimal feature selection method when combined with the Naive Bayes algorithm is the Particle Swarm Optimization (PSO) method with an average accuracy value of 89.08%.
References
R. Mahendrajaya, G. A. Buntoro, and M. B. Setyawan, “Analisis Sentimen Pengguna Gopay Menggunakan Metode Lexicon Based Dan Support Vector Machine,†Komputek, vol. 3, no. 2, p. 52, 2019, doi: 10.24269/jkt.v3i2.270.
H. Himawan, W. Kaswidjanti, A. Sentimen, M. Sosial, and L. Based, “Metode Lexicon Based Dan Support Vector Machine Untuk Menganalisis Sentimen Pada Media Sosial Sebagai Rekomendasi Oleh-Oleh Favorit,†vol. 2018, no. November, pp. 235–244, 2018.
A. K. Fauziyyah, “Analisis Sentimen Pandemi Covid19 Pada Streaming Twitter Dengan Text Mining Python,†J. Ilm. SINUS, vol. 18, no. 2, p. 31, 2020, doi: 10.30646/sinus.v18i2.491.
F. Ratnawati, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,†INOVTEK Polbeng - Seri Inform., vol. 3, no. 1, p. 50, 2018, doi: 10.35314/isi.v3i1.335.
S. Rahayu, J. J. Purnama, H. M. Nawawi, and F. S. Nugraha, “Algoritma Naïve Bayes Classifier Untuk Memprediksi Gejala Autism Spectrum Disorders Pada Anak-Anak,†Pros. Semin. Nas. Rekayasa dan Teknol. (TAU SNAR-TEK), vol. ISSN:, no. November, 2019.
D. A. Muthia, “Sentiment Analysis on Closure of Illegal Movie Streaming Sites Using Naïve Bayes Algorithm,†J. Pilar Nusa Mandiri, vol. 16, no. 1, pp. 123–128, 2020, doi: 10.33480/pilar.v16i1.1306.
L. R. Putri, M. S. Mubarok, and Adiwijaya, “Klasifikasi Sentimen Ulasan Buku Berbahasa Inggris Menggunakan Information Gain Dan Naive Bayes,†e-Proceeding Eng., vol. 4, 2017.
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.
A. W. Attabi, L. Muflikhah, and M. A. Fauzi, “Penerapan Analisis Sentimen untuk Menilai Suatu Produk pada Twitter Berbahasa Indonesia dengan Metode Naïve Bayes Classifier dan Information Gain,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4548–4554, 2018.
H. S. Utama, D. Rosiyadi, D. Aridarma, and B. S. Prakoso, “Sentimen Analisis Kebijakan Ganjil Genap Di Tol Bekasi Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain,†J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 247–254, 2019, doi: 10.33480/pilar.v15i2.705.
A. Syakuro, “Analisis Sentimen Masyarakat Terhadap E-Commerce Pada Media Sosial Menggunakan Metode Naive Bayes Classifier ( NBC ) Dengan Seleksi Fitur Information Gain ( IG ), pp. 1–89, 2017.
M. R. T. Zaman, “Klasifikasi Opini Terhadap Kebijakan Publik Merdeka Belajar Pada Jejaring Sosial Twitter Menggunakan Metode Naive Bayes Dengan Seleksi Fitur Information Gain,†2020.
R. Khalida and S. Setiawati, “Analisis Sentimen Sistem E-Tilang Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain,†J. Inform. Inf. Secur., vol. 1, no. 1, pp. 19–26, 2020, doi: 10.31599/jiforty.v1i1.137.
S. Widya Sihwi, I. Prasetya Jati, and R. Anggrainingsih, “Twitter Sentiment Analysis of Movie Reviews Using Information Gain and Naïve Bayes Classifier,†Proc. - 2018 Int. Semin. Appl. Technol. Inf. Commun. Creat. Technol. Hum. Life, iSemantic 2018, pp. 190–195, 2018.
Syahriani, A. A. Yana, and T. Santoso, “Sentiment analysis of facebook comments on indonesian presidential candidates using the naïve bayes method,†J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012012.
A. Andilala, “Movie Review Sentimen Analisis Dengan Metode Naïve Bayes Base on Feature Selection,†Pseudocode, vol. 3, no. 1, pp. 1–9, 2016, doi: 10.33369/pseudocode.3.1.1-9.
A. Taufik, “Optimasi Particle Swarm Optimization Sebagai Seleksi Fitur Pada Analisis Sentimen Review Hotel Berbahasa Indonesia Menggunakan Algoritma Naïve Bayes,†J. Tek. Komput. AMIK BSI, vol. III, no. 2, pp. 40–47, 2017.
K. S. Nugroho, I. Istiadi, and F. Marisa, “Naive Bayes classifier optimization for text classification on e-government using particle swarm optimization,†J. Teknol. dan Sist. Komput., vol. 8, no. 1, pp. 21–26, 2020, doi: 10.14710/jtsiskom.8.1.2020.21-26.
R. Y. Hayuningtyas and R. Sari, “Analisis Sentimen Opini Publik Bahasa Indonesia Terhadap Wisata Tmii Menggunakan Naïve Bayes Dan Pso,†J. Techno Nusa Mandiri, vol. 16, no. 1, pp. 37–42, 2019.
Y. Cahyono, “Analisis Sentiment pada Sosial Media Twitter Menggunakan Naїve Bayes Classifier dengan Feature Selection Particle Swarm Optimization dan Term Frequency,†J. Inform. Univ. Pamulang, 2017.
K. Solecha, “Analisa Sentimen Dengan Algoritma Naïve Bayes Classifier Berbasis Particle Swarm Optimization Untuk Review Restoran,†J. Speed – Sentra Penelit. Eng. dan Edukasi, vol. 11, no. 1.
Betesda, “Peningkatan Optimasi Sentimen Dalam Pelaksanaan Proses Pemilihan Presiden Berdasarkan Opini Publik Dengan Menggunakan Algoritma Naive Bayes Dan Particle Swarm Optimization,†2020.
R. Aulianita and A. Rifai, “Optimasi Particle Swarm Optimization Pada Naive Bayes Untuk Sentiment Analysis Furniture,†Inf. Manag. Educ. Prof., vol. 3, no. 1, pp. 31–40, 2018.
S. A. Saputra, “Sentiment Analysis Analisis Sentimen E-Wallet Pada Google Play Menggunakan Algoritma Naive Bayes Berbasis Particle Swarm Optimization,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 377–382, 2019.
R. Hidayat, “Kombinasi Seleksi Fitur Chi Square Dengan Algoritma Naive Bayes Untuk Analisis Sentimen,†vol. 1, pp. 72–81, 2017.
R. Y. Kisworini, “Peningkatan Performa Naive Bayes Dengan Seleksi Atribut Menggunakan Chi Square Untuk Klasifikasi Loyalitas Pelanggan GRAB,†J. Informatics, Inf. Syst. Softw. Eng. Appl., vol. 2, no. 2, pp. 69–75, 2020.
A. Z. Amrullah, A. Sofyan Anas, and M. A. J. Hidayat, “Analisis Sentimen Movie Review Menggunakan Naive Bayes Classifier Dengan Seleksi Fitur Chi Square,†Jurnal, vol. 2, no. 1, pp. 40–44, 2020.
A. Nisa, E. Darwiyanto, and I. Asror, “Analisis Sentimen Menggunakan Naive Bayes Classifier dengan Chi-Square Feature Selection Terhadap Penyedia Layanan Telekomunikasi,†e-Proceeding Eng., vol. 6, no. 2, pp. 8650–8659, 2019.
A. Nugroho, “Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram,†J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 2, no. 2, p. 200, 2018.
W. C. Indhiarta, “Penggunaan N-Gram Pada Analisa Sentimen,†pp. 1–18, 2017.
D. Irvantoro, I. Saifudin, and R. Umilasari, “Feature Selection Menggunakan Chi Squared dan N-Gram Dengan Algoritma Naive Bayes Classifier Untuk Analisis Sentimen Review Produk Elektronik,†vol. 53, no. 9, pp. 1689–1699, 2019.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
