Particle Swarm Optimization-Based Support Vector Machine Method For Sentiment Analysis in OVO Digital Payment Applications

Particle Swarm Optimization-Based Support Vector Machine Method For Sentiment Analysis in OVO Digital Payment Applications

• By Sari, Retno; Hayuningtyas, Ratih Yulia;
• Jurnal Nasional Terakreditasi
• 122
• 232-239

Sentiment analysis is used to analyze reviews of a place or item from an  application or website that then classified the review into positive reviews or negative reviews. reviews from users are considered very important because it contains information that can make it easier for new users who want to choose the right digital payment. Reviews about digital payment ovo are so much that it is difficult for prospective users of ovo digital payment applications to draw conclusions about ovo digital payment information. For this reason, a classification method is needed in this study  using support vector machine and PSO methods. In this study, we used 400 data that were reduced to 200 positive reviews and 200 negative reviews. The accuracy obtained by using the support vector machine method of 76.50% is in the fair classification, while the accuracy obtained by using the support vector machine and Particle Swarm Optimization (PSO) method is 82.75% which is in good classification.

• Katakunci PSO; SVM; OVO; Sentiment; Analysis
• URL http://ejournal.uniks.ac.id/index.php/JTOS/article/view/1776/1341
• Penerbit Generation Journal, Kediri Jawa Timur

REFERENSI

A. Agrani and B. Rikumahu, “Perbandingan Analisis Sentimen Terhadap Digital Payment ‘go-pay’ Dan ‘ovo’ Di Media Sosial Twitter Menggunakan Algoritma Naive Bayes Dan Word Cloud,” eProceedings Manag., vol. 7, no. 2, pp. 2534–2542, 2020.  S. Surohman, S. Aji, R. Rousyati, and F. F. Wati, “Analisa Sentimen Terhadap Review Fintech Dengan Metode Naive Bayes Classifier Dan K-  Nearest Neighbor,” EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 93–105, 2020.  E. S. Basryah, A. Erfina, C. Warman, D. Digital, and P. Store, “ANALISIS SENTIMEN APLIKASI DOMPET DIGITAL DI ERA 4 . 0 PADA MASA PENDEMI COVID-19 DI PLAY STORE,” pp. 189–196, 2021.  M. Tri Anjasmoros and dan Fitri Marisa, “Analisis Sentimen Aplikasi Go-Jek Menggunakan Metode Svm Dan Nbc (Studi Kasus: Komentar Pada Play Store),” Conf. Innov. Appl. Sci. Technol. (CIASTECH 2020), no. Ciastech, pp. 489–498, 2020.  N. Yunita, “Analisis Sentimen Berita Artis Dengan Menggunakan Algoritma Support Vector Machine dan Particle Swarm Optimization,” J. Sist. Inf. STMIK Antar Bangsa, vol. 5, no. 2, pp. 104–112, 2016.  V. K. S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 162–170, 2020.  S. W. Yudha and M. Wahyudi, “Komparasi Algoritma Klasifikasi Untuk Analisis Sentimen Review Film Berbahasa Asing,” Semin. Nas. Inform. Sist. Inf. Dan Keamanan Siber, pp. 180–185, 018.  D. Gunawan, D. Riana, D. Ardiansyah, F. Akbar, and S. Alfarizi, “Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023,” J. Tek. Komput. AMIK BSI, vol. VI, no. 1, pp. 121–129, 2020.  K. A. Rokhman, B. Berlilana, and P. Arsi, “Perbandingan Metode Support Vector Machine Dan Decision Tree Untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online,” J. Inf. Syst. Manag., vol. 3, no. 1, pp. 1–7, 2021.  F. Sodik and I. Kharisudin, “Analisis Sentimen dengan SVM , NAIVE BAYES dan KNN untuk Studi T anggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter,”  Prisma, vol. 4, pp. 628–634, 2021.  Gorunescu, (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer, 2011.