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All Journal Jusikom : Jurnal Sistem Komputer Musirawas
Hendri Purnomo
Universitas Darmajaya Lampung
Computer Science & IT

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Evaluasi Kinerja Algoritma Naïve Bayes, KNN, dan SVM dalam Analisis Sentimen Media Sosial: Evaluasi Kinerja Algoritma Naïve Bayes, KNN, dan SVM dalam Analisis Sentimen Media Sosial Sylvia Sylvia; Hendri Purnomo; Oki Arifin; Atika Arpan; Rizka Permata; Dwi Handoko; Fitriyah Fitriyah
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 9 No. 2 (2024): Jusikom : Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v9i2.2447

Abstract

Social media sentiment analysis has become increasingly important with the rise of platforms like Twitter and Facebook as sources of public opinion. This study evaluates the performance of three machine learning algorithms—Naïve Bayes, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM)—in classifying sentiment from social media data. Using a dataset in Indonesian, we apply cross-validation techniques to measure accuracy, precision, recall, F1-score, and computation time for each algorithm. The results show that SVM achieves the highest accuracy and F1-score, while Naïve Bayes offers better computational speed. KNN demonstrates the lowest performance in terms of accuracy and efficiency. These findings provide guidance for practitioners and researchers in selecting the appropriate algorithm for sentiment analysis based on their specific needs.
Co-Authors Atika Arpan Dwi Handoko Fitriyah Fitriyah Oki Arifin Rizka Permata Sylvia Sylvia