Perbandingan SVM dan Naive Bayes untuk Analisis Sentimen Media Sosial
DOI:
https://doi.org/10.29407/8hjf6s59Abstract
Analisis sentimen pada media sosial telah menjadi bidang penelitian yang berkembang pesat seiring meningkatnya volume data teks yang dihasilkan pengguna. Penelitian ini melakukan kajian literatur sistematis (Systematic Literature Review/SLR) terhadap 56 jurnal ilmiah yang membahas penerapan algoritma Naive Bayes dan Support Vector Machine (SVM) dalam analisis sentimen pada berbagai platform media sosial. Tujuan penelitian ini adalah membandingkan kinerja kedua algoritma berdasarkan metrik akurasi, precision, recall, dan F1-score, serta mengidentifikasi faktor-faktor yang mempengaruhi performanya. Hasil kajian menunjukkan bahwa SVM secara konsisten menghasilkan akurasi lebih tinggi dibandingkan Naive Bayes, dengan rata-rata akurasi SVM berkisar 83%–97% dan Naive Bayes 56%–95%. Faktor yang mempengaruhi performa kedua algoritma meliputi ukuran dan kualitas dataset, metode preprocessing teks, teknik ekstraksi fitur (TF-IDF, Word2Vec, n-gram), serta karakteristik bahasa. SVM unggul dalam menangani data berdimensi tinggi dan non-linear, sementara Naive Bayes menawarkan efisiensi komputasi lebih baik pada dataset kecil. Penelitian ini memberikan panduan pemilihan algoritma yang tepat sesuai kebutuhan analisis sentimen media sosial
Keywords:
analisis sentimen, media sosial, naive bayes, support vector machine, systematic literature review##plugins.themes.default.displayStats.downloads##
References
[1] K. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in
software engineering,” Tech. Rep. EBSE 2007-001, 2007.
[2] R. Firmansyah et al., “Perbandingan Kinerja Algoritma SVM, LSTM, dan Fine-tuned IndoBERT
dalam Analisis Sentimen Opini Masyarakat Indonesia terhadap Mobil Listrik,” Indonesian Journal of
Computer Science Research, 2026.
[3] A. Pramudita et al., “Analisis Sentimen dengan Pendekatan Ensemble Learning dan Word
Embedding pada Twitter,” Journal of Information System Management (JOISM), 2023.
[4] Y. Saputra et al., “Evaluation ChatGPT User Sentiment using Naive Bayes: A Review of
Confusion Matrix and Classification Report,” Jurnal Riset Sistem dan Teknologi Informasi (RESTIA),
2025.
[5] B. Santoso et al., “Classification of JKN Mobile User Sentiment with Support Vector Machine,
Naive Bayes and Logistic Regression,” IEEE, 2025.
[6] D. Pratama et al., “Sentiment Analysis on Indonesian Telegram Reviews Using Naive Bayes,
SVM, Random Forest, and Boosting Models,” IEEE, 2024.
[7] M. Hidayat et al., “Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce
Menggunakan Algoritma K-Nearest Neighbor,” JPIT, 2023.
[8] S. Rahayu et al., “Analisis Sentimen pada Review Pengguna E-Commerce Bidang Pangan
Menggunakan Metode Support Vector Machine,” SENAMIKA, 2021.
[9] T. Wibowo et al., “Analisis Sentimen Aplikasi Mybca Melalui Review Pengguna Di Google Play
Store Menggunakan Algoritma Naive Bayes,” Prosiding SEMNAS INOTEK, 2024.
[10] A. Kurnia et al., “Analisis Sentimen Pengguna Terhadap Aplikasi Glints Di Google Play Store
Menggunakan Metode Naive Bayes,” JURNAL MEDIA AKADEMIK, 2025.
[11] R. Siregar et al., “Sentiment Analysis of Tourist Reviews at Waterfront City Pangururan Using
Naive Bayes and TF-IDF Algorithm,” Journal of Software Engineering, Information and Communication
Technology, 2025.
[12] F. Nugraha et al., “Harmonizing Public Sentiment Analysis on Indonesia's New Capital (IKN) on
X Using Machine Learning,” IEEE, 2025.
[13] H. Lestari et al., “Indonesia Digital Voice: MyPertamina Sentiment Analysis with Machine
Learning,” IEEE, 2026.
[14] P. Setiawan et al., “Analisis Sentimen Pengguna X Indonesia Terkait Kendaraan Listrik
Menggunakan IndoBERT,” Jurnal Ilmu Komputer dan Sistem Informasi, 2024.
[15] M. Putri et al., “Analisis Sentimen Aplikasi BRImo pada Ulasan Pengguna Di Google Play
Menggunakan Algoritma Naive Bayes,” JATI, 2023.
[16] I. Santosa et al., “Reviewing of Online Grocery Shopping in Review Sentiment Classification on
the Sayurbox App on the Play Store using Naive Bayes Algorithm,” IEEE, 2025.
[17] W. Dewi et al., “Analisis Sentimen Ulasan Pelecehan Di Media Sosial TikTok Menggunakan
SVM dan K-Means Clustering,” Jurnal Informatika dan Teknik Elektro Terapan, 2025.
[18] B. Irawan et al., “Analisis Sentimen Cyberbullying pada Media Sosial Twitter menggunakan
Metode Support Vector Machine dan Naive Bayes Classifier,” TECHSI, 2023.
[19] R. Hakim et al., “Analisis Sentimen Masyarakat Terhadap Kasus Judi Online Menggunakan Data
dari Media Sosial X,” Jurnal Sistem Informasi dan Informatika (Simika), 2025.
[20] A. Mahmud et al., “Analisis Sentimen Komentar Twitter(X) tentang Kesehatan Mental
menggunakan Naive Bayes,” Prosiding SEMNAS INOTEK, 2025.
[21] N. Kusuma et al., “Analisis Sentimen Petani Milenial Pada Media Sosial X Menggunakan
Algoritma Support Vector Machine,” JPTI, 2025.
[22] O. Wahyudi et al., “Analisis Sentimen Media Sosial X Terhadap Kebijakan Presiden Republik
Indonesia Prabowo Subianto,” JURNAL FASILKOM, 2025.
[23] G. Pratiwi et al., “Analisis Sentimen pada Media Sosial Terhadap Layanan Samsat Digital
Nasional dengan Support Vector Machine,” Idealis, 2025.
[24] S. Ningsih et al., “Sentiment Analysis of Seabank Application Reviews on Google Play Store
Using Naive Bayes Classifier,” IEEE, 2024.
[25] T. Hartono et al., “Sentiment Analysis on Game Review on the Steam Platform using Support
Vector Machine, TF-IDF and Chi-Square Methods,” IEEE, 2025.
[26] B. Rahardjo et al., “Aspect-Based Sentiment Analysis of Indonesian ChatGPT Reviews with
Multinomial Naive Bayes,” IEEE, 2025.
[27] C. Puspita et al., “Sentiment Analysis of Islamic Worship Applications on Google Play Store using
Naive Bayes and SVM,” IEEE, 2024.
[28] D. Saputro et al., “Analisis Sentimen Media Sosial Menggunakan Algoritma BERT dan LSTM,”
JOCSIT, 2025.
[29] E. Kartika et al., “Analisis Sentimen dalam Tokopedia Terhadap Ulasan Pelanggan Menggunakan
Metode Naive Bayes,” Prosiding SEMNAS INOTEK, 2025.
[30] F. Mardiansyah et al., “Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial
Twitter,” Jurnal DINDA, 2021.
[31] G. Firmanto et al., “Analisis Sentimen Publik di Media Sosial Terhadap Kenaikan PPN 12% di
Indonesia Menggunakan IndoBERT,” JKBTI, 2025.
[32] H. Sulistyo et al., “Analisis Sentimen Terhadap Pelaksanaan Pilkada 2024 pada Media Sosial
YouTube Menggunakan Metode Decision Tree,” JINTEKS, 2025.
[33] I. Permana et al., “Opini Publik Pasca-Pemilihan Presiden: Eksplorasi Analisis Sentimen Media
Sosial X Menggunakan SVM,” SINTECH JOURNAL, 2024.
[34] J. Widodo et al., “Optimasi Parameter Support Vector Machine dengan Algoritma Genetika Untuk
Analisis Sentimen Pada Media Sosial Instagram,” SINTECH JOURNAL, 2023.
[35] K. Ramadhan et al., “Analisis Sentimen Ulasan Media Sosial UMKM Kuliner dengan Pendekatan
Lexicon-Based,” Jurnal Informatika Polinema, 2026.
[36] L. Mulyani et al., “Analisis Sentimen Review pada Aplikasi Media Sosial TikTok Menggunakan
Algoritma K-NN dan SVM Berbasis PSO,” JIK, 2023.
[37] M. Apriani et al., “Sentiment Analysis of Artificial Intelligence (AI) On Twitter Social Media
Using Lexicon Based Method,” JUTSI, 2023.
[38] N. Susanto et al., “Analisis Sentimen Masyarakat Di Media Sosial X Terhadap Kemenkes Dengan
Naive Bayes dan SVM,” Jurnal Sains dan Teknologi, 2025.
[39] O. Handayani et al., “Analisis Sentimen Pada Media Sosial Menggunakan Metode Support Vector
Machine,” JITKOM, 2025.
[40] P. Nasution et al., “Analisis Sentimen Pinjaman Online Di Media Sosial Twitter Menggunakan
Metode Naive Bayes,” KLIK, 2023.
[41] Q. Pratama et al., “Sentiment Analysis of GOJEK User Reviews Using SVM, Naive Bayes, and
Random Forest Models,” IEEE, 2025.
[42] R. Fitria et al., “Sentiment Analysis of Beauty Product Reviews on the Female Daily Website
Using Naive Bayes and Word2Vec,” IEEE, 2025.
[43] S. Gunawan et al., “Analyzing public sentiment on sustainability: A comprehensive review,”
Natural Language Processing Journal, 2024.
[44] T. Haryanto et al., “Sentimen Analisis Belajar Online di Twitter Menggunakan Naive Bayes,”
JANAPATI, 2022.
[45] U. Setiawan et al., “Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di
Media Sosial Twitter,” Komputika, 2023.
[46] V. Nurhidayah et al., “Analisis Sentimen untuk Deteksi Ujaran Kebencian pada Media Sosial
Terkait Pemilu 2024,” JATI, 2024.
[47] W. Rosyida et al., “Analisis Sentimen Kaum LGBT pada Media Sosial Twitter Menggunakan
Algoritma Naive Bayes,” Jurnal Teknik Informatika, 2022.
[48] X. Surya et al., “Analisis Sentimen Masyarakat terhadap Layanan Shopeefood Melalui Media
Sosial Twitter dengan Algoritma Naive Bayes Classifier,” JOINS, 2022.
[49] Y. Maulana et al., “Analisis Sentimen Tanggapan Publik Mengenai E-Tilang di YouTube
Menggunakan Naive Bayes,” IKRAITH Informatika, 2023.
[50] Z. Ibrahim et al., “Analisis Sentimen Pengguna Media Sosial terhadap Kebijakan Kenaikan Pajak
Hiburan Menggunakan SVM,” JIPI, 2024.
[51] A. Rahayu et al., “Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja
Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naive Bayes,” JURNAL MEDIA
INFORMATIKA BUDIDARMA, 2024.
[52] B. Purwanto et al., “Analisis Sentimen Tanggapan Public Mengenai E-Tilang Melalui Media
Sosial YouTube Menggunakan Algoritma Naive Bayes,” IKRAITH INFORMATIKA, 2023.
[53] C. Anggraini et al., “Analisis Sentimen Akhir Masa Jabatan Presiden Jokowi pada Media Sosial
X Menggunakan Naive Bayes,” SKANIKA, 2025.
[54] D. Maharani et al., “Analisis Sentimen Terhadap Berhentinya TiktokShop pada Media Sosial
Twitter Menggunakan Algoritma Naive Bayes,” JATI, 2024.
[55] E. Khairani et al., “Analisis Sentimen Terhadap Media Sosial Twitter dengan Kasus Kampanye
Anti-Korupsi di Indonesia Menggunakan Naive Bayes,” JURNAL MEDIA INFORMATIKA
BUDIDARMA, 2024.
[56] F. Zulkarnain et al., “Analisis Sentimen Terhadap Event Big Sale 11.11 Shopee di Media Sosial
Instagram Menggunakan Metode Naive Bayes,” Buletin Sistem Informasi dan Teknologi Islam, 2023.
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