Rachmad Mahendrajaya, Ghulam Asrofi Buntoro, Moh Bhanu Setyawan


Go-Pay is part of the Gojek application and one of the most popular finteches in Indonesia. Although the most popular, not all users have positive or even negative comments. Now users can submit various media opinions, one of which is Twitter. Twitter media has the advantage of a simple display, updated topics, open access to tweets and express opinions quickly. From a variety of comments on Twitter it takes a technique to divide into classes positive or negative opinions. This study uses prepocessing and labeling opinions into positive and negative classes with the lexicon Based method. As for the classification using the Support Vector Machine (SVM) method. The data used in the form of opinions about Go- Pay reviews from social media Twitter, amounting to 1210. The results of labeling with Lexicon Based amounted to 923 for positive and 287 for negative. While the classification of the SVM method using the Linear kernel produces 89.17% and 84.38% for the Polynomial kernel.


Sentiment Analysis, Twitter, Go-Pay, Lexicon Based, Support Vector Machine (SVM)


Josi, A., Abdillah, L. A., & Suryayusra. 2014. Studi, P., Informatika, T., Komputer, F. I., Darma, U. B., ... No, Y . (n.d.). PENERAP AN TEKNIK WEB SCRAPING PADA MESIN PENCARI ARTIKEL ILMIAH.

Buntoro, G. A. (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. Integer Journal Maret, 1(1), 32–41. Retrieved from hulam_Buntoro/publication/316617194 _Analisis_Sentimen_Calon_Gubernur_ DKI_Jakarta_2017_Di_Twitter/links/5 907eee44585152d2e9ff992/Analisis- Sentimen-Calon-Gubernur-DKI- Jakarta-2017-Di-Twitter.pdf

Yosmita Praptiwi, D. (2018). Analisis Sentimen Online Review Pengguna E- Commerce Menggunakan Metode Support Vector Machine Dan Maximum Entropy.

Dailysocial 2018 : Fintech Report. (2018). Website : -report-2018

Tirto 2019 : Evolusi gojek sebagai fintech (2019). Website : fintech-lewat-go-pay-cAvw

APJII 2018 : Penetrasi & Perilaku Pengguna Internet Indonesia (2018). Website :

Novantirani, A., Sabariah, M. K., & Effendy, V. (2015). Analisis Sentimen pada Twitter untuk Mengenai Penggunaan Transportasi Umum Darat Dalam Kota dengan Metode Support Vector Machine. E-Proceeeding of Engineering, 2(1), 1–7.

Github : Id Stopwords. Website : OpinionWords

Github : kamus. Website : Sentimen-ID/tree/master/kamus

Himawan, H., Kaswidjanti, W., Sentimen, A., Sosial, M., & Based, L. (2018). Metode Lexicon Based Dan Support Vector Machine Untuk Menganalisis Sentimen Pada Media Sosial Sebagai Rekomendasi Oleh-Oleh Favorit. 2018(November), 235–244.

Rofiqoh, U., Perdana, R. S., & Fauzi, M. A. (2017). Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexion Based Feature. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J- PTIIK) Universitas Brawijaya, 1(12), 1725–1732. Retrieved from http://j- ptiik/article/view/628

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DOI: 10.24269/jkt.v3i2.270



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