SENTIMENT ANALYSIS ON GOJEK AND GRAB USER REVIEWS USING SVM ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION

SENTIMENT ANALYSIS ON GOJEK AND GRAB USER REVIEWS USING SVM ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION

• By Hermanto; Kuntoro, Antonius Yadi; Asra, Taufik; Nurajijah; Effendi, Lasman; Ocanitra, Ridatu;
• Jurnal Nasional Terakreditasi
• 165
• 117-122

Users of the Gojek and Grab application can provide reviews or comments about the application on Google Play. Reviews in the form of giving opinions about their satisfaction or dissatisfaction with the services provided. So with the many opinions provided, making people selective in choosing an online motorcycle taxi service provider. The application with the best review will be chosen by the community. In previous studies regarding the classification of online ojek service review using the Naïve Bayes algorithm, C.45 and Random Forest produced an unsatisfactory accuracy of 69.18% at the highest value. This study aims to determine the extent of
the analysis of Gojek and Grab application user reviews based on user comments by classifying negative and positive reviews with a higher level of accuracy than previous studies so that applications with the best reviews can be known for public consideration in using the application's services. The method used for data review classification is using the Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO). The test results on the Grab application review get the highest accuracy results in the amount of 73.09%
with AUC value = 0.804, while for the test results on the application review Gojek get an accuracy value of 65.59% and AUC value = 0.680

• Katakunci Sentiment Analysis, Gojek and Grab Reviews, Support Vector Machine, Particle Swarm Optimization
• URL http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/1304
• Penerbit ,

REFERENSI

Aggarwal, C. C. (2015). Data Mining The Textbook. Springer.
Basaria, Hasan, A. S., Hussina, B., Anantaa, I. G. P., & Zeniarj, J. (2012). Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. Malaysian Technical Universities Conference on Engineering & Technology 2012, 453 – 462.
Hadna, Shiddieqy, N. M., Santosa, P. I., & Winarno, W. W. (2016). Literature study on comparative methods to process.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques. In Data Mining. https://doi.org/10.1016/b978-0-12-381479-1.00001-0
Hermanto, H., Kuryanti, S. J., & Khasanah, S. N. (2019). Comparison of Naïve Bayes Algorithm, C4.5 and Random Forest for Classification in Determining Sentiment for Ojek Online Service. SinKron, 3(2), 266–274. http://www.polgan.ac.id/jurnal/index.php/ sinkron/article/view/235
Hermanto, Kuntoro, A. Y., Asra, T., Nurajijah, Effendi, L., & Ocanitra, R. (2020). Final Report of Independent Research: Sentiment Analysis on Gojek And Grab User Reviews Using The Support Vector Machine Algorithm Based On Particle Swarm Optimization.
Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011). An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, 8(2), 191– 200. https://doi.org/10.1016/S1672-6529(11)60020-6
Nurajijah, N., & Riana, D. (2019). Algoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah. Jurnal Teknologi Dan Sistem Komputer, 7(2), 77–82. https://doi.org/10.14710/jtsiskom.7.2.2019 .77-82
Salam, A., Zeniarja, J., & Khasanah, R. S. U. (20 18). Facebook Social Media Comment sentiment analysis with K-Nearest Neighbor (case study on Expedition service account J & T Indonesia Ekspress). SINTAK 2018.
https://doi.org/ISBN: 978-602-8557-20-7