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All Journal JOIV : International Journal on Informatics Visualization Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Juisi (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Information Technology (JIfoTech) Jurnal Abdimas Bina Bangsa DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal INFOTEL SAINTEK Infact: Jurnal Sains dan Komputer DEVOTE: Jurnal Pengabdian Masyarakat Global
Haeni Budiati
Universitas Kristen Immanuel
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)

 1 
Pengembangan Model Klasifikasi Sentimen Dengan Pendekatan Vader dan Algoritma Naive Bayes Terhadap Ulasan Aplikasi Indodax Agus Dirgahayu Zendrato; Sunneng Sandino Berutu; Yo’el Pieter Sumihar; Haeni Budiati
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5050

Abstract

Cryptocurrency trading applications such as Indodax have grown rapidly, the understanding of user sentiment towards the platform is still lacking, so it is interesting to analyze user sentiment towards the platform. To measure sentiment, this research proposes a combined approach of Vader and Naïve Bayes methods. The data used is a collection of user comments on the google play store platform related to user experience using Indodax. The Vader method is used to analyze sentiment directly from the comment text, while Naïve Bayes is adopted to improve accuracy in sentiment classification. The sentiment analysis process involves various steps, starting from data preparation, data pre-processing, labeling of training and testing data and performance evaluation of the Naive Bayes model. At the sentiment analysis stage with the Vader Sentiment method, the positive category obtained the highest percentage of 63.5%, followed by the neutral category at 18.9% and negative at 17.6%. Meanwhile, based on the performance evaluation of the Naïve Bayes model, the accuracy value is 78% while the highest precision value is achieved by the negative sentiment category at 80% and recall in the positive sentiment category at 44%.
Co-Authors A. R. Himamunanto Agus Dirgahayu Zendrato Agustinus Rudatyo Himamunanto Anan Sosmita Lase Antonius Bima Murti Wijaya Apriani Apriani Barita Suci Vernando Zebua Delima Zai Farelia Devi Julia Putri Fornieli Gulo Gogor C Setyawan Gogor Christmass Setyawan Heri Wijayanto Hermas Rizzal Indriyani Hulu Jatmika Jatmika Jatmika Johni Revormasi Ziliwu Joseph Supriadi Juli Supriyanto Gea Kristian Juri Damai Lase Liefson Jacobus, Liefson Lukas Agung Heriputranto Peter1 Pius Hermanto Nehe Sunneng Sandino Berutu Surjawirawan Dwiputranto Timothy Ageng Pracaya Yeong-Chin Chen Yo’el Pieter Sumihar