Social Media X-Based Public Opinion Analysis of Prabowo-Gibran Government's First 100 Days Using Naïve Bayes and K-Nearest Neighbor Classification Methods
Abstract
This study investigates public sentiment toward the first 100 days of the Prabowo–Gibran administration by analyzing opinions expressed on X (formerly Twitter) using machine learning approaches. A total of 431 valid tweets were collected, preprocessed, and manually labeled into positive and negative categories. The results reveal that 62% of public sentiment was negative, while 38% was positive, indicating widespread public criticism during the administration’s early period. Two algorithms, Naïve Bayes and K-Nearest Neighbor (KNN), were applied to classify sentiment. The Naïve Bayes model achieved superior performance, with an accuracy of 97.22%, compared to KNN’s 62.65%. The probabilistic nature of Naïve Bayes allowed it to manage high-dimensional, imbalanced textual data effectively, while KNN suffered from the “curse of dimensionality” and class bias. These findings demonstrate that Naïve Bayes remains a reliable and computationally efficient model for political sentiment analysis in the Indonesian digital context. Despite its strengths, this study acknowledges limitations in manual labeling and linguistic nuances such as sarcasm and irony. Future research is encouraged to integrate deep learning architectures like LSTM or BERT and adopt aspect-based sentiment analysis to capture more contextual insights from political discourse.
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Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.
Informatics Engineering Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.
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Total: 20 References- Al-Areef, M. H., & Saputra, S. K. (2023). Analisis sentimen pengguna Twitter mengenai calon presiden Indonesia tahun 2024 menggunakan algoritma LSTM. Jurnal SAINTIKOM, 22(1), 1–10.
- Asro’i, A., & Februariyanti, H. (2022). Analisis sentimen pengguna Twitter terhadap perpanjangan PPKM menggunakan metode K-Nearest Neighbor. Jurnal Khatulistiwa Informatika, 10(1), 25–32.
- Era, D., & Andryana, S. (2021). Perbandingan algoritma Naïve Bayes dan K-Nearest Neighbor pada analisis sentimen pembukaan pariwisata di masa pandemi COVID-19. Jurnal Sains Komputer & Informatika (J-SAKTI), 5(2), 856–865.
- Hakim, Z. R., & Sugiyono. (2024). Analisa sentimen terhadap Kereta Cepat Jakarta–Bandung menggunakan algoritma Naïve Bayes dan K-Nearest Neighbor. Jurnal Sains dan Teknologi, 5(3), 1–10.
- Hidayat, S. (2022). Analisa sentimen masyarakat Indonesia terhadap kinerja Prabowo Subianto sebagai anggota Kabinet Indonesia Maju di platform Twitter menggunakan R dengan algoritma Naïve Bayes. JUNIF: Jurnal Nasional Informatika, 3(1), 1–8.
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