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Yuniar, Eny
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Computer Science & IT

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Perbandingan Metode Naive Bayes, Random Forest dan SVM Untuk Analisis Sentimen Pada Twitter Tentang Kenaikan Gaji Guru Yuniar, Eny; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6970

Abstract

The increase in teacher salaries has become a highly debated issue within the community, with various opinions being expressed through social media, particularly Twitter. This study aims to analyze public sentiment regarding the teacher salary increase policy using three machine learning algorithms: Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The data used consists of 6010 tweets collected on the topic, which were processed into 5531 data points after cleaning and preprocessing. This study evaluates the performance of each algorithm using accuracy, precision, recall, and F1-score metrics. The results show that SVM achieved the highest accuracy (86%) before applying the SMOTE technique, followed by Random Forest (85%) and Naïve Bayes (84%). After applying SMOTE to address data imbalance, Random Forest showed a significant performance improvement, with accuracy reaching 99%, followed by SVM (98%) and Naïve Bayes (89%). These results indicate that the SMOTE technique can effectively improve model performance, particularly in handling the imbalance between positive, negative, and neutral sentiment data. This study provides new insights into how the public responds to the teacher salary increase policy, while also introducing the use of SMOTE to enhance accuracy in sentiment analysis on social media.
Co-Authors Nirwana Hendrastuty