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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu JSAI (Journal Scientific and Applied Informatics) Informanika Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Aksi Kita: Jurnal Pengabdian Kepada Masyarakat JCOSIS (Journal Computer Science and Information Systems)
Fitri Purwaningtias
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : JSAI (Journal Scientific and Applied Informatics)

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Eksplorasi Model Prediksi Sentimen Postingan Di Media Sosial Fitri Purwaningtias
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7513

Abstract

Sentiment analysis is a text analysis technique that can be used to understand the opinion, feeling, or sentiment of a text. This research aims to explore and compare sentiment prediction models on social media data with three algorithms, namely GaussianNB, Logistic Regression, and Support Vector Machine (SVM). The dataset used is taken from www.kaggle.com, which consists of social media posts from the Twitter, Facebook, and Instagram platforms with positive, negative, and neutral sentiment categories. The analysis process involves text data preprocessing, data labeling, feature extraction with Bag of Words (BoW) and TF-IDF, and handling data imbalance with SMOTE. The results showed that the SVM model with TF-IDF and SMOTE performed best, with 93.25% accuracy on training data and 92.50% on test data. This research contributes to determining the best model for sentiment analysis of social media data and can be a reference in developing better sentiment prediction systems in the future.
Co-Authors AA Sudharmawan, AA Ahmad Dzulfaqqor Faishol Ahmad Mutatkin Bakti Eka Kirana Putri Eka Puji Agustini Fajar Romadhan Imam Solikin Irman Effendy Linda Atika Maria Ulfa Maria Ulfa1 Muhammad Fadhil Pratama, Rian Sapta