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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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SENTIMENT ANALYSIS ON GOJEK AND GRAB USER REVIEWS USING SVM ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION
Analisis Sentimen Tentang Ulasan Pengguna Gojek Dan Grab Menggunakan Algoritma Svm Berdasarkan Optimasi Swarm Partikel
Keywords:
Sentiment Analysis, Gojek and Grab Reviews, Support Vector Machine, Particle Swarm Optimization
Abstract
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
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Published
2020-03-31
How to Cite
Hermanto, H., Kuntoro, A., Asra, T., Nurajijah, N., Effendi, L., & Ocanitra, R. (2020). SENTIMENT ANALYSIS ON GOJEK AND GRAB USER REVIEWS USING SVM ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION. Jurnal Pilar Nusa Mandiri, 16(1), 117-122. https://doi.org/10.33480/pilar.v16i1.1304
Section
Articles
Copyright (c) 2020 Hermanto Hermanto, Antonius Yadi Kuntoro, Taufik Asra, Nurajijah Nurajijah, Lasman Effendi, Ridatu Ocanitra
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