SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION
Sentiment analysis is an area of approach that solves problems by using reviews from various relevant scientific perspectives. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. The number of reviews is certainly very much making consumers reluctant to read reviews. Eventually, the reviews become useless. For this reason, the authors classify based on positive and negative classes, so consumers can find product comparisons quickly and precisely. The implementation of Particle Swarm Optimization (PSO) optimization can improve the accuracy of the Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm can improve accuracy and provide solutions to the review classification problem to be more accurate and optimal. Comparison of accuracy resulting from testing this data is an SVM algorithm of 89.20% and AUC of 0.973, then compared to SVM based on PSO with an accuracy of 94.60% and AUC of 0.985. The results of testing the data for the NB algorithm are 88.50% accuracy and AUC is 0.536, then the accuracy is compared with the PSO based NB for 0.692. In these calculations prove that the application of PSO optimization can improve accuracy and provide more accurate and optimal solutions
Buntoro, & Asrofi, G. (2016). ANALISIS SENTIMEN HATESPEECH PADA TWITTER DENGAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE. Jurnal Dinamika Informatika, 5(2), 1–12. http://ojs.upy.ac.id/ojs/index.php/dinf/article/view/975
Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter | Buntoro | INTEGER: Journal of Information Technology. INTEGER: Journal of Information Technology, 2(1), 32–41. https://ejurnal.itats.ac.id/integer/article/view/95
Chandra, D. N., Indrawan, G., & Sukaraja, I. N. (2016). Klasifikasi Berita Lokal Radar Malang Menggunakan Metode Naïve Bayes Dengan Fitur N-Gram. Jurnal Ilmiah Teknologi Dan Informasia ASIA (JITIKA), 10(1), 11–19. https://www.jurnal.stmikasia.ac.id/index.php/jitika/article/view/62
Faid, M., Jasri, M., & Rahmawati, T. (2019). Perbandingan Kinerja Tool Data Mining Weka dan Rapidminer Dalam Algoritma Klasifikasi. Teknika, 8(1), 11–16. https://doi.org/https://doi.org/10.34148/teknika.v8i1.95
Kristiyanti, D. A. (2015). ANALISIS SENTIMEN REVIEW PRODUK KOSMETIK MELALUI KOMPARASI FEATURE SELECTION. Konferensi Nasional Ilmu Pengetahuan Dan Teknologi Tahun 2015, 69–76. http://konferensi.nusamandiri.ac.id/prosiding/index.php/knit/article/view/33
Kusmira, M. (2019). ANALISIS SENTIMEN REGISTRASI ULANG KARTU SIM PADA TWITTER MENGGUNAKAN ALGORITMA SVM DAN K-NN | INTI Nusa Mandiri. INTI Nusa Mandiri, 14(1), 105–110. http://ejournal.nusamandiri.ac.id/index.php/inti/article/view/541
Mahendrajaya, R., Buntoro, G. A., & Setyawan, M. B. (2019). ANALISIS SENTIMEN PENGGUNA GOPAY MENGGUNAKAN METODE LEXICON BASED DAN SUPPORT VECTOR MACHINE. KOMPUTEK, 3(2), 52. https://doi.org/10.24269/jkt.v3i2.270
Pudjiarti, E. (2016). PREDIKSI SPAM EMAIL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN PARTICLE SWARM OPTIMIZATION. Jurnal Pilar Nusa Mandiri, 12(2), 171–181. http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/271
Rinawati, R. (2013). PENERAPAN PARTICLE SWARM OPTIMIZATION UNTUK SELEKSI ATRIBUT PADA METODE SUPPORT VECTOR MACHINE UNTUK PENENTUAN PENILAIAN KREDIT. Seminar Nasional Ilmu Pengetahuan Dan Teknologi Komputer, 73. http://konferensi.nusamandiri.ac.id/prosiding/index.php/sniptek/article/view/309
Rustiana, D., & Rahayu, N. (2017). ANALISIS SENTIMEN PASAR OTOMOTIF MOBIL: TWEET TWITTER MENGGUNAKAN NAÏVE BAYES. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 8(1), 113–120. https://doi.org/10.24176/simet.v8i1.841
Vinodhini, G., & Chandrasekaran, R. M. (2016). Comparative performance evaluation of a neural network-based approach for sentiment classification of online reviews. Journal of King Saud University - Computer and Information Sciences, 28(1), 2–12. https://doi.org/10.1016/j.jksuci.2014.03.024
Abstract viewed = 38 times
PDF downloaded = 10 times
Copyright (c) 2020 Zulia Imami Alfianti, Deni Gunawan, Ahmad Fikri Amin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Jurnal Riset Informatika as the publisher of the journal, and the author also holds the copyright without restriction.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases, and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , are allowed with written permission from Jurnal Riset Informatika.
Jurnal Riset Informatika, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Jurnal Riset Informatika are the sole and exclusive responsibility of their respective authors and advertisers.