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Safitri, Tiara
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Energy Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine Safitri, Tiara; Umaidah, Yuyun; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5039

Abstract

Twitter is often used as a source of public opinion and sentiment data for analysis, where the data can be used to understand public opinion about a topic. Sentiment analysis is widely used in various fields, one of which is in the marketing field. a company can carry out a sentiment analysis of the public figures they want to make Brand Ambassadors (BA), which later these sentiments can be taken into consideration for them to be able to determine the BA of their products. Sentiment analysis can also be used to distinguish the attitude of customers, users or followers towards a brand, topic, or product with the help of their reviews. Based on this, this study will analyze the sentiments of Twitter users towards music group BTS, using the Knowledge Discovery Database (KDD) research methodology, with 5 stages namely Data Selection, Data Preprocessing, Data Transformation, Text Mining and Evaluation. By using the Support Vector Machine (SVM) algorithm with a linear kernel, this study will do 3 scenarios with the distribution of training data and testing data 90:10 in scenario 1, 80:20 in scenario 2, and 70:30 in scenario 3. Confusion Matrix is used to evaluate the performance of the algorithm used and the results show that the best performance of the model formed is in scenario 1 and scenario 2.
Co-Authors Adoe, Cenneth Bai Ahmad Rahbani Sulaiman S Aidhia Rahmi Amjahmudin, Ibnu Anggia, Khasiva Aryani, Komang Putri Aulia, Alfina Deviani Deviani Edy Haryono Fahreza Fahreza, Fahreza Fatika, Neza Ghyffary, Moch. Dhaffa Abudhzar Iing Rika Yanti Iqbal Maulana Katon, Ayu Sri Kemala, Qori Dwi Khoirunnisyah, Khoirunnisyah Kurniawan, Muhammad Nurwenta Lasmono , Arsyah Poetra Lubis, Asep Ridwan Marpaung, Teli Hosana Mimi Rosadi Mufidah, Rintan Roro Nadya, Khairunnisa Najmi, Pyona Faddila Netania Emilisa Nursamsu Putri, Wanda Andella Ramadhani, Akbar Ramadhani, Nia sandra dewi Sarfilianty Anggiani, Sarfilianty Setyaki, Omar Liandro Aryo Simatupang, Sonya Restauli Siska Rita Mahyuni Siwi, Galuh Praka Susanti, Oktora Syarief, Yunita Iriani Tamaulina, Salsabila Tiarani, Yesha Ibanez Ulfa , Khairani Umaidah, Yuyun Utami, Raden Ayu Wahyuningtiyas, Noni Febriastri Yasri, Budi Zulkarnain Zulkarnain