Perbandingan Naïve Bayes dan SVM untuk Analisis Sentimen Ulasan Kompas.id pada Data Tidak Seimbang
DOI:
https://doi.org/10.47065/bulletincsr.v6i1.929Keywords:
Sentiment Analysis; Naïve Bayes; Support Vector Machine; TF-IDF; Kompas.idAbstract
The rapid advancement of digital technology and the increasing use of mobile devices have driven the widespread adoption of digital news applications, including Kompas.id. User reviews on the Google Play Store represent an important data source for understanding user satisfaction and emerging issues; however, the large volume of reviews makes manual analysis inefficient. Therefore, this study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying Kompas.id user reviews into positive, neutral, and negative sentiments. The research employs the Knowledge Discovery in Databases (KDD) framework, which includes web scraping, text preprocessing, lexicon-based sentiment labeling, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification and evaluation stages. The dataset consists of 1,023 cleaned reviews after data preprocessing. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results indicate that Naïve Bayes achieves an accuracy of 72%, while SVM outperforms it with an accuracy of 80%, reflecting its stronger ability to handle high-dimensional and sparse textual feature spaces. Word cloud visualization reveals that positive sentiments are mainly associated with content quality, whereas negative sentiments are dominated by subscription-related issues and technical problems. Based on these findings, SVM is recommended as a more effective algorithm for sentiment analysis of digital news application reviews.
Downloads
References
S. Kemp, “Digital 2025: Global Overview Report.” Diakses: 25 Oktober 2025. [Daring]. Tersedia pada: https://datareportal.com/reports/digital-2025-global-overview-report
W. Medhat, A. Hassan, dan H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, hlm. 1093–1113, Des 2014, doi: 10.1016/J.ASEJ.2014.04.011.
B. Liu, “Sentiment Analysis and Opinion Mining,” Morgan & Claypool Publishers, 2012.
U. Fayyad, G. Piatetsky-Shapiro, dan P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Mag, vol. 17, no. 3, hlm. 37–37, Mar 1996, doi: 10.1609/AIMAG.V17I3.1230.
K. Ravi dan V. Ravi, “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications,” Knowl Based Syst, vol. 89, hlm. 14–46, Nov 2015, doi: 10.1016/J.KNOSYS.2015.06.015.
C. M. Bishop, “Pattern Recognition and Machine Learning,” 2006.
M. Mohri, A. Rostamizadeh, dan A. Talwalkar, “Foundations of Machine Learning,” 2012.
M. V. Mäntylä, D. Graziotin, dan M. Kuutila, “The evolution of sentiment analysis—A review of research topics, venues, and top cited papers,” Comput Sci Rev, vol. 27, hlm. 16–32, Feb 2018, doi: 10.1016/J.COSREV.2017.10.002.
R. Syahputra, G. J. Yanris, dan D. Irmayani, “SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter,” Sinkron, vol. 7, no. 2, hlm. 671–678, Mei 2022, doi: 10.33395/sinkron.v7i2.11430.
M. E. Apriyani, A. F. Nur, dan E. S. Astuti, “Performance Comparison of Naïve Bayes and SVM Algorithms in Sentiment Analysis on JKN Application Data,” Knowbase?: International Journal of Knowledge in Database, vol. 4, no. 2, hlm. 180–188, Des 2024, doi: 10.30983/KNOWBASE.V4I2.8758.
B. F. Wiguna, H. Herlawati, dan A. Y. P. Yusuf, “Sentiment Analysis of On-Demand Ride-Hailing Systems using Support Vector Machine and Naïve Bayes,” PIKSEL?: Penelitian Ilmu Komputer Sistem Embedded and Logic, vol. 11, no. 2, hlm. 401–414, Sep 2023, doi: 10.33558/PIKSEL.V11I2.7384.
J. W. Iskandar dan Y. Nataliani, “Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 6, hlm. 1120–1126, Des 2021, doi: 10.29207/resti.v5i6.3588.
S. A. S. Mola, D. L. B. Baun, I. O. Nunes, dan M. M. A. R. Sani, “ANALISIS SENTIMEN APLIKASI HALO BCA DI GOOGLE PLAY STORE MENGGUNAKAN METODE NAIVE BAYES, SUPPORT VECTOR MACHINE DAN RANDOM FOREST,” HOAQ (High Education of Organization Archive Quality)?: Jurnal Teknologi Informasi, vol. 15, no. 2, hlm. 69–79, Des 2024, doi: 10.52972/hoaq.vol15no2.p69-79.
M. I. Fikri, T. S. Sabrila, Y. Azhar, dan U. M. Malang, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” SMATIKA JURNAL, vol. 10, no. 02, hlm. 71–76, Des 2020, doi: 10.32664/SMATIKA.V10I02.455.
S. Chohan, A. Nugroho, A. M. B. Aji, dan W. Gata, “Analisis Sentimen Pengguna Aplikasi Duolingo Menggunakan Metode Naïve Bayes dan Synthetic Minority Over Sampling Technique,” Paradigma - Jurnal Komputer dan Informatika, vol. 22, no. 2, hlm. 139–144, Sep 2020, doi: 10.31294/p.v22i2.8251.
M. I. Ghozali, W. H. Sugiharto, dan A. F. Iskandar, “Analisis Sentimen Pinjaman Online Di Media Sosial Twitter Menggunakan Metode Naive Bayes,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 3, no. 6, hlm. 1340–1348, Jun 2023, doi: 10.30865/KLIK.V3I6.936.
G. Salton dan C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf Process Manag, vol. 24, no. 5, hlm. 513–523, Jan 1988, doi: 10.1016/0306-4573(88)90021-0.
A. McCallum dan K. Nigam, “A comparison of event models for naive bayes text classification,” AAAI Conference on Artificial Intelligence, 1998.
T. Joachims, “Text categorization with Support Vector Machines: Learning with many relevant features,” hlm. 137–142, 1998, doi: 10.1007/BFB0026683.
G. A. Miller, “WordNet,” Commun ACM, vol. 38, no. 11, hlm. 39–41, Nov 1995, doi: 10.1145/219717.219748.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Naïve Bayes dan SVM untuk Analisis Sentimen Ulasan Kompas.id pada Data Tidak Seimbang
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Muhammad Ardana, Rini Mayasari, Iqbal Maulana
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
