Perbandingan Naïve Bayes dan Support Vector Machine pada Klasifikasi Sentimen Tokopedia Di Playstore
DOI:
https://doi.org/10.61805/fahma.v24i3.230Keywords:
Sentiment Analysis, Naïve Bayes, Support Vector Machine, TF-IDF, Hyperparameter TuningAbstract
Tokopedia is one of the largest e-commerce platforms in Indonesia, generating a growing number of user reviews that makes manual sentiment analysis inefficient. This study compares the performance of Naïve Bayes and Support Vector Machine (SVM) in classifying sentiment in Tokopedia application reviews. Data were collected using the google-play-scraper library and automatically labeled based on user ratings, with ratings of 1–2 classified as negative and 4–5 as positive. After preprocessing, including case folding, cleaning, tokenization, stopword removal, and stemming using Sastrawi, 1,212 reviews were obtained, comprising 609 negative and 603 positive reviews. Text features were extracted using TF-IDF, and the models were evaluated using 5-fold cross-validation. With default parameters, Naïve Bayes and SVM achieved accuracies of 75.99% and 74.83%, respectively. After GridSearchCV tuning, Naïve Bayes achieved 76.65% accuracy and 73.28% F1-score, while SVM achieved 77.81% accuracy and 76.51% F1-score. These results show that hyperparameter tuning affects model comparison, with tuned SVM slightly outperforming Naïve Bayes.
Downloads
References
B. Z. Ramadhan, I. Riza, and I. Maulana, “Analisis Sentimen Ulasan pada Aplikasi E-Commerce dengan Menggunakan Algoritma Naïve Bayes,” Journal of Applied Informatics and Computing, vol. 6, no. 2, pp. 220–225, 2022, doi: 10.30871/jaic.v6i2.4725.
M. Yunus, M. Husni, and M. M. Mufadhdhal, “Klasifikasi Sentimen terhadap BPJS pada Media Sosial Twitter Menggunakan Naive Bayes,” SMATIKA Jurnal, vol. 11, no. 2, pp. 81–91, 2021, doi: 10.32664/smatika.v11i02.577.
A. Agustian, T. Tukino, and F. Nurapriani, “Penerapan Analisis Sentimen dan Naive Bayes terhadap Opini Penggunaan Kendaraan Listrik di Twitter,” Jurnal TIKA, vol. 7, no. 3, pp. 243–249, 2022.
S. D. Prasetyo, S. S. Hilabi, and F. Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,” Jurnal KomtekInfo, vol. 10, no. 1, pp. 1–7, 2023, doi: 10.35134/komtekinfo.v10i1.330.
A. Muzaki, V. Febriana, and W. N. Cholifah, “Analisis Sentimen pada Ulasan Produk di E-Commerce dengan Metode Naive Bayes,” Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI), vol. 5, no. 4, pp. 758–761, 2024, doi: 10.30998/jrami.v5i4.9647.
G. Darmawan, S. Alam, and M. I. Sulistyo, “Analisis Sentimen Berdasarkan Ulasan Pengguna Aplikasi MyPertamina pada Google Playstore Menggunakan Metode Naïve Bayes,” STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer, vol. 2, no. 3, pp. 100–108, 2023, DOI: https://doi.org/10.55123/storage.v2i3.2333
R. L. Atimi and E. E. Pratama, “Implementasi Model Klasifikasi Sentimen pada Review Produk Lazada Indonesia,” Jurnal Sains dan Informatika, vol. 8, no. 1, pp. 88–96, 2022, doi: 10.34128/jsi.v8i1.419.
K. A. Rokhman, Berlilana, and P. Arsi, “Perbandingan Metode Support Vector Machine dan Decision Tree untuk Analisis Sentimen Review Komentar pada Aplikasi Transportasi Online,” JOISM, vol. 2, no. 2, 2021, doi: 10.24076/joism.2021v3i1.341.
I G. S. D. Putra and I N. T. A. Putra, “Implementasi Metode Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Mobile Kita Bisa,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 2, 2025, doi: https://doi.org/10.23960/jitet.v13i2.6423
A. Halim Hasugian, M. Fakhriza, and D. Zukhoiriyah, “Analisis Sentimen Pada Review Pengguna E-Commerce Menggunakan Algoritma Naïve Bayes,” Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD, vol. 6, no. 1, pp. 98–107, 2023. doi: https://doi.org/10.53513/jsk.v6i1.7400
A. N. Puspitasari, Y. Findawati, and Y. Rahmawati, “Analisis Sentimen Tweet Pengguna E-Commerce dengan Metode Klasifikasi Naïve Bayes,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1123–1132, 2024, doi: https://doi.org/10.29100/jipi.v9i3.4939
Hajaroh, T. Suprapti, dan R. Narasati, "Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Produk Makanan dan Minuman di Tokopedia," JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 111–118, 2024. Doi: https://doi.org/10.36040/jati.v8i1.8237
M. R. Adrian, M. P. Putra, M. H. Rafialdy, dan N. A. Rakhmawati, "Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB," Jurnal Informatika UPGRIS, vol. 7, no. 1, pp. 36–40, 2021. doi: https://doi.org/10.26877/jiu.v7i1.7099
I. S. K. Idris, Y. A. Mustofa, dan I. A. Salihi, "Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Menggunakan Algoritma Support Vector Machine (SVM)," Jambura Journal of Electrical and Electronics Engineering, vol. 5, no. 1, pp. 32–35, Jan. 2023. doi https://doi.org/10.37905/jjeee.v5i1.16830
M. Y. R. Gaja, I. Maulana, and O. Komarudin, “Analisis Sentimen Opini Pengguna Aplikasi Vidio pada Ulasan Playstore Menggunakan Algoritma Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 4, pp. 2767–2774, 2023, doi : https://doi.org/10.36040/jati.v7i4.7197
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Igo Fiqi Nurwahid, Untung Subagyo (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
