Pangenalan penggunaan Data Science untuk melakukan Analisis Sentimen di SMAN 1 Tanjung Bintang

Nirwana Hendrastuty, m. ghufroni an'ars, Damayanti Damayanti, Fitrah Amalia, Samuel Hutagalung, Chris Mario, M. Tova


Currently, various groups and ages, almost all Indonesian people own and use social media as a means of obtaining and conveying information to the public. This is an opportunity for data scientists to carry out sentiment analysis on topics or opinions conveyed by the public. Sentiment analysis is used to find out whether the opinion or post has a positive, negative or neutral meaning. Unfortunately there are still many students who do not know this. From these problems the servant team provides a solution to conduct data science training, especially in sentiment analysis of public figures. The target of this activity is students of class XI and XII from SMAN 1 Tanjung Bintang. The implementation of community service for the assisted school scheme consists of three stages, namely, preparation, implementation and evaluation. The preparatory stage is carried out by carrying out a survey first to find out the problems experienced by partners and carry out an analysis to find solutions to these problems. From the results of the distribution of questionnaires in the training, it was found that an increase in understanding of data science, especially sentiment analysis, was an average of 1.8, previously students got an average pre-test of 5.1 and a post-test of 6.9.

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Arfat, M. F., Nurkholis, A., & Kurniawan, I. (2022). Analisis Sentimen Masyarakat Indonesia Terkait Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Metode Support Vector Machine (SVM). Jurnal Informatika.

Buwono, B. T., Matondang, N., & Raya, J. R. F. (n.d.). Analisis Sentimen Pada Media Sosial Twitter Mengenai Kebijakan Kenaikan Harga Bahan Bakar Minyak Menggunakan Metode Naive Bayes.

Cahyani, R. R., & Cahyani, R. (2020). Analisis Sentimen Pada Media Sosial Twitter Terhadap Tokoh Publik Peserta Pilpres 2019. MATICS, 12(1), 79.

Fauzi, A., Akbar, M. F., & Asmawan, Y. F. A. (2019). Sentimen Analisis Berinternet Pada Media Sosial dengan Menggunakan Algoritma Bayes. Jurnal Informatika, 6(1), 77–83.

Giovani, A. P., Ardiansyah, A., Haryanti, T., Kurniawati, L., & Gata, W. (2020). Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma KLASIFIKASI. Jurnal Teknoinfo, 14(2), 115.

Hendrastuty, N., Isnain, A. R., & Rahmadhani, A. Y. (2021). Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine. Jurnal Informatika.

Hendrastuty, N., & Sn, A. (2021). Text Summarization in Multi Document Using Genetic Algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(4), 327.

Isnain, A. R., Hendrastuty, N., & Andraini, L. (2021). Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis. Jurnal Informatika, 5.

Permatasari, P. A., Linawati, L., & Jasa, L. (2021). Survei Tentang Analisis Sentimen Pada Media Sosial. Majalah Ilmiah Teknologi Elektro, 20(2), 177.



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Copyright (c) 2023 Nirwana Hendrastuty, M. Ghufroni An’ars, Damayanti, Fitrah Amalia, Samuel Hutagalung, Chris Mario, M. Tova

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Journal of Engineering and Information Technology for Community Service (JEIT-CS)
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