Penerapan NLP Menggunakan Algoritma Naive Bayes, C4.5, XGBoost untuk Analisis Sentimen Ulasan Produk Kecantikan di Tokopedia dan Shopee

Srikandi Alifya; Jasmir Jasmir; Elvi yanti

doi:10.61132/prosemnasproit.v2i2.71

Authors

Srikandi Alifya Universitas Dinamika Bangsa
Jasmir Jasmir Universitas Dinamika Bangsa
Elvi yanti Universitas Dinamika Bangsa

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.71

Keywords:

analisis sentimen, Naive Bayes, NLP, C4.5, XGBoost

Abstract

The growth of e-commerce in Indonesia has led to an increase in product reviews, including for beauty products on Tokopedia and Shopee. These reviews serve as important sources of information to assess consumer satisfaction; however, manually analyzing thousands of reviews daily is impractical. This study applies Natural Language Processing (NLP) with Naive Bayes, C4.5, XGBoost algorithms to classify sentiment in Indonesian-language reviews. The dataset used consists of 76,256 reviews labeled as positive, negative, and neutral. The research stages include text preprocessing, feature representation using BoW and TF-IDF, data balancing through SMOTE, and model performance evaluation based on accuracy, precision, and recall. Differences in results among the algorithms were analyzed using ANOVA. The results show that Naive Bayes achieved the highest accuracy at 67.71%, followed by XGBoost at 65.91%, and C4.5 at 58.39%, with Naive Bayes performing best in identifying positive and negative sentiments, while XGBoost and C4.5 handled more complex data patterns effectively. These findings provide guidance for sentiment analysis in Indonesian and support businesses in obtaining automated insights from customer reviews to improve product quality and services.

References

Agustina, N., Citra, D. H., Purnama, W., Nisa, C., & Kurnia, A. R. (2022). Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 2(1), 47–54. https://doi.org/10.57152/malcom.v2i1.195

Albab, M. U., P., Y. K., & Fawaiq, M. N. (2023). Optimization of the Stemming Technique on Text Preprocessing President 3 Periods Topic. Jurnal Transformatika, 20(2), 1–12. https://doi.org/10.26623/transformatika.v20i2.5374

Arsadhana, M., Efendi, B., & Trihudiyatmanto, M. (2025). Analisis Kepuasan Pelanggan Melalui Sentimen Ulasan Menggunakan Algoritma Naive Baye S. Jurnal Magisma, XIII(1), 1–8.

Aufar, A. F., Mochamad Alfan Rosid, Eviyanti, A., & Astutik, I. R. I. (2023). Optimizing Text Preprocessing for Accurate Sentiment Analysis on E-Wallet Reviews. JICTE (Journal of Information and Computer Technology Education), 7(2), 42–50. https://doi.org/10.21070/jicte.v7i2.1650

Azhima, T., Siswa, Y., Informatika, T., Sains, F., Muhammadiyah, U., & Timur, K. (2023). Komparasi Optimasi Chi-Square , CFS , Information Gain Dan ANOVA Dalam Evaluasi Peningkatan Akurasi Algoritma Klasifikasi Data Performa Akademik Mahasiswa. 18(1).

Chaerul, M., Triyono, G., Komputer, M. I., Informasi, F. T., & Luhur, U. B. (2025). Analisis Sentimen Kebijakan Pembatasan Subsidi Bahan Bakar Minyak di Indonesia Tahun 2024 Menggunakan Algoritma Klasifikasi The Sentiment Analysis of the Fuel Subsidy Limitation Policy Using Support Vector Classifier and Random Forest Classifier Algorithm. 5(5), 1471–1484.

Dwi Prasetyo, A., & Betty Yel, M. (2024). Analisa Sentimen Menggunakan Algoritma C4.5 Dan Naïve Bayes Dengan Topik Artificial Intelligence Pada Media Sosial Twitter (X) Sentiment Analysis Using C4.5 and Naïve Bayes Algorithms With Artificial Intelligence Topics on Social Media Twitter (X). Journal of Information Technology and Computer Science (INTECOMS), 7(5), 1610–1617.

Gunawan, B., Pratiwi, Sasty, H., & Pratama, Esyudha, E. (2023). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. Jurnal Edukasi Dan Penelitian Informatika, 2(1), 95–103.

Haekal, B. V., Ernawati, I., & Chamidah, N. (2021). Klasifikasi Kepuasan Pengguna Layanan Aplikasi Shopee Menggunakan Metode Decision Tree C4.5. Informatik : Jurnal Ilmu Komputer, 17(3), 188. https://doi.org/10.52958/iftk.v17i3.3648

Lukhayu Pritalia, G. (2018). Penerapan Algoritma C4.5 untuk Penentuan Ketersediaan Barang E-commerce. Indonesian Journal of Information Systems, 1(1), 47–56. https://doi.org/10.24002/ijis.v1i1.1727

Muzaki, A., Febriana, V., & Cholifah, W. N. (2024). Analisis Sentimen Pada Ulasan Produk di E-Commerce dengan Metode Naive Bayes. Jurnal Riset Dan Aplikasi Mahasiswa Informatika (JRAMI), 5(4), 758–765. https://doi.org/10.30998/jrami.v5i4.9647

Nadia Dwi Oktaviani1, R. H. (2025). Pengaruh Kualitas Produk Dan Ewom Terhadap Keputusan Pembelian Skincare Pada E-Commerce Shopee. Digital Business and Entrepreneurship Journal Vol., 3(1), 36–46.

Najibah Agus Ratri, B., & Arum Sari, Y. (2021). Analisis Sentimen Review Produk Kecantikan menggunakan Metode Naïve Bayes. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(12), 2548–2964. http://j-ptiik.ub.ac.id

Oecd, K. (2025). 기후 개발 재원 동향 1) 1. 5(1), 1–7. https://doi.org/10.33330/j-com.v5i1.3570

Recurrent, M., & Network, N. (2022). Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan. 5(1), 31–38.

Salsabila, S. M., Alim Murtopo, A., & Fadhilah, N. (2022). Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier. Jurnal Minfo Polgan, 11(2), 30–35. https://doi.org/10.33395/jmp.v11i2.11640

Setyaningsih, A. F., Septiyani, D., & Widiasari, S. R. (2023). Implementasi Algoritma Naïve Bayes untuk Analisis Sentimen Masyarakat pada Twitter mengenai Kepopuleran Produk Skincare di Indonesia. Jurnal Teknologi Informatika Dan Komputer, 9(1), 224–235. https://doi.org/10.37012/jtik.v9i1.1409

Studi Informatika, P., Matematika dan Ilmu Pengetahuan Alam, F., Raya Kampus UNUD, J., Jimbaran, B., & Selatan, K. (2025). Komparasi Ekstraksi Fitur BoW dan TF-IDF untuk Klasifikasi SMS Menggunakan Naive Bayes I Komang Dwiprayoga a1 , Made Agung Raharja a2. Jnatia, 3(2), 247–254.

Syafi’i, A., Afdal, M., Saputra, E., & Novita, R. (2024). Analisis Sentimen Ulasan Pengguna Aplikasi Penjualan Pulsa Menggunakan Algoritma Naïve Bayes Classifier. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(3), 1300–1308. https://doi.org/10.32493/jtsi.v7i3.41364

Tribuana, D., Baharuddin, B., & Muhammad Resky, A. (2025). Penerapan Algoritma XGBoost Untuk Prediksi Kepuasan Pelanggan Pada Layanan E-Commerce: Studi Pada Dataset Transaksi Nyata. Jurnal Teknologi Dan Bisnis Cerdas, 1(1), 50–59. https://doi.org/10.64476/jtbc.v1i1.5

Valen, R., Bagus, I., & Mahendra, M. (2023). Membandingkan Analisis Sentimen Review Pelanggan Shopee Dan Tokopedia Menggunakan Google ’ s NLP API. 11(4), 655–662.

Wati, H. L., Anggraeni, N., Kolbiah, S., Hendar, U., & Agustina, N. (2025). PERBANDINGAN ALGORITMA RANDOM FOREST DAN NARATIF : Jurnal Ilmiah Nasional Riset Aplikasi dan Teknik Informatika. 07(01), 64–71.

Wibisono, A. C., Nadira, T. S., & Sutabri, T. (2025). Analisis Sentimen Pelanggan pada Platform Shopee Menggunakan Metode Naive Bayes. Nusantara Journal of Multidisciplinary Science, 2(6), 1259–1266. https://jurnal.intekom.id/index.php/njms