Classification of Customer Reviews on the MitraShopee Application Using Support Vector Machine and Naïve Bayes Methods
Klasifikasi Ulasan Pelanggan pada Aplikasi MitraShopee Menggunakan Metode Support Vector Machine dan Naïve Bayes
DOI:
https://doi.org/10.21070/ups.8313Keywords:
Classification, Reviews, Customers, Support Vector Machine, Naïve BayesAbstract
This study aims to classify customer reviews pf the MitraShopee application into three categories critism, suggestions, and questions using Naïve Bayes and Support Vector Machine (SVM) algorithms. A total od 6.000 reviews were collected from Google Play Store and processed through data cleaning stages such as case folding, tokenizing, stopword removal, and normalization using Python in Google Colab. The cleanes data was then analyzed with TF-IDF and labeled using a rule-based approach. Evaluation results showed that the SVM algorithm performed better with an accuracy of 81%, while Naïve Bayes achieved 56% accuracy. SVM demonstrated a balanced performance in terms of precision and recall, particularly in detecting suggestion and question reviews. Meanwhile, Naïve Bayes tended to be biased toward a single class and struggled to accurately identify criticisms. This research highlights the importance of algorithm selection and through preprocessing in text classification. The results are expected to support app developpers in quickly and accurately understanding user needs through an automated review classification system.
Downloads
References
Saifudin, and M. Hadziq Affan, “Analisa Pemasaran Platform P2p Lending Syariah Pada Pt Alami Fintek Sharia,” Jurnal Ekonomi dan Perbankan Syariah, vol. 8, no. 1, pp. 436–448, 2023, doi: 10.30651/jms.v8i1.17706.
Febby Adelia Irawan, Aldy Rialdy Atmadja, and Agung Wahana, “Analisis Sentimen Ulasan Aplikasi Bank Digital Menggunakan Algoritma Naive Bayes,” Journal of Computer Science and Information Technology, vol. 4, no. 2, pp. 60–68, 2024. doi: 10.31949/infotech.v10i2.11801
Mahda Nurhamidah, “Pengaruh Complaint Handling Bank Syariah Indonesia Terhadap Kepuasan dan Loyalitas Nasabah Ex BNI Syariah dan Ex BRI Syariah,” July 2024, repository: https://repository.uinjkt.ac.id/dspace/handle/123456789/62339
Amalia Elma Sari, Sri Widowati, and Kemas Muslim Lhaksmana, “Klasifikasi Ulasan Pengguna Aplikasi Mandiri Online di Google Play Store dengan Menggunakan Metode Information Gain dan Naive Bayes Classifier,” e-Proceeding of Engineering, vol. 6, no. 2, pp. 9143–9157, 2019.
Z. S. Li, N. N. Arony, K. Devathasan, M. Sihag, N. Ernst, and D. Damian, “Unveiling the Life Cycle of User Feedback: Best Practices from Software Practitioners,” Sep. 2023, [Online]. Available: http://arxiv.org/abs/2309.07345
M. Qamal and W. Fuadi, “Analisis Sentimen Toko Online Menggunakan Algoritma Naive Bayes Classifier,” pp. 641–650.
Ipan Saepul Milal. M. Hasanudin, M. Aliffiallathifa Nur Azhari, Rifki Aditya Nugraha, Nova Agustina, and Sri Erina Damayanti, “Klasifikasi Teks Review Pada E-Commerce Tokopedia Menggunakan Algoritma SVM,” Jurnal Ilmiah Nasional Riset Aplikasi dan Teknik Informatika vol. 05, no. 01, 2023, doi: 10.53580/naratif.v5i1.191.
Albert Lodewyk Sentosa Siahaan, dkk, “E–Commerce,” ISBN: 978–623–151–879–8, no. 225/JTE/2021, November 2023.
Prasyadi Wibawa Rahayu, dkk, “Buku Ajar Data Mining,” ISBN: 978–623–8483–96–9, no. 006/JBI/2023, Januari 2024.
Detty Purnamasari, dkk, “Pengantar Metode Analisis Sentimen,” 2023.
Laurensia Simanihuruk, and Hari Suparwito, “Long Short-Term Memory and Bidirectional Long Short-Term Memory Algorithms for Sentiment Analysis of Skintific Product Reviews,” ITM Web Conf., vol. 71, 2025, doi: 10.1051/itmconf/20257101016.
Pooja Saigal, “Support Vector Machines Evolution and Aplications,” ISBN: 978-1-53618-865-3, 2021.
Poomrape Poomka, Nittaya Kerdprasop, and Kittisak Kerdprasop, “Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews,” International Journal of Machine Learning and Computing, vol. 11, no. 02, pp 103-109, 2021, doi: 10.18178/ijmlc.2021.11.2.1021
Alshaf Pebrianggara, Istian Almanfaluti and Wahyu Karobby, “Design Thinking For Business,” ISBN: 78-623-464-077-9, no. 218/JTI/2019, September 2023
Irna Putri Rahayu, Ahmad Fauzi, and Jamaludin Indra, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 2, p. 296, Dec. 2022, doi: 10.30865/json.v4i2.5381.
Ayu Sri Rahayu, Ahmad Fauzi, and Rahmat, “Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 2, p. 349, Dec. 2022, doi: 10.30865/json.v4i2.5398.
Nurhaliza Agustina. C.A, Desy Herlina Citra, dkk, “Implementasi Algoritma Naïve Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 2, pp. 47–54, 2022, doi: 10.57152/malcom.v2i1.195.
Ahyawa Aulia Khoirin Nisa, “Pengaruh Promosi, Harga, Kualitas Layanan, Kualitas Produk Terhadap Kepuasan Pelanggan Shopee: Sebuah Kajian Konseptual,” Jurnal Riset Manajemen, vol. 1, no. 3, pp. 315–328, Sep. 2023, doi: 10.54066/jurma.v1i3.899.
Elisa Febriyani, and Herny Februariyanti, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naive Bayes Classifier Di Twitter,” Jurnal Tekno Kompak, vol. 17, no. 1, pp. 25–38, 2023, doi: 10.33365/jtk.v17i1.2061
Wachid Darmawan, Muhammad Faizal Kurniawan, Wahyu Setianto, and Wim Hapsoro, “Analisis Sentimen Penerapan Kurikulum Merdeka Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor Dengan Forward Selection,” Jurnal Smart Comp, vol. 12, no. 1, pp. 245–253, 2023, doi: 10.30591/smartcomp.v12i1.4634
Hennie Tuhuteru, “Analisis Sentimen Masyarakat Terhadap Pembatasan Sosial Berksala Besar Menggunakan Algoritma Support Vector Machine,” Journal Information System Development, vol. 5, no. 2, pp. 7–13, 2020.
Muhammad Ibnu Alfarizi, Lailis Syafaah, and Merinda Lestandy, “Emotional Text Classification Uing TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory),” JUITA : Jurnal Informatika, vol. 10, no. 2, pp. 225-232, 2022, doi: 10.30595/juita.v10i2.13262
Muhammad Mujahid, Erol Kina, dkk, “Data Oversampling and Imbalanced Datasets: As Investigation Of Performance for Machine Learning and Feature Engineering,” Journal of Big Data, vol. 11. no. 87, 2024, doi: 10.1186/s40537-024-00943-4
Zahra Bami, Ali Behnampour, and Hassan Doosti, “A New Flexible Train-Test Split Algorithm, an Approach For Choosing Among the Hold-out, K-fold Cross-validation, and Hold-out Interation,” vol. 01, 2025, doi: 10.48850/arXiv.2501.06492
I Gede Bintang Arya Budaya, and I Ketut Putu Suniantara, “Comparison of Sentiment Analysis Algorithms with SMOTE Oversampling and TF-IDF Implementation on Google Reviews for Public Health Centers,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, iss. 3, pp. 1077-1086, 2024, doi: 10.57152/Malcom.v4i3.1459
Amjad Iqbal, Rashid Amin, dkk, “Sentiment Analysis of Consumer Reviews Using Deep Learning,” MDPI Journal, vol. 14, iss. 17, 2022, doi: 10.3390/su141710844
Biyang Guo, Songqiao Han, dkk, “Label Confusion Learning to Enhance Text Classification Model,” Association for the Advancement of Artificial Intelligence, pp. 12929-12936, 2020, doi: 10.48550/arXiv.2012.04987
Satria Budi, “Implementasi Metode Naïve Bayes Untuk Klasifikasi Ulasan Pada Aplikasi Telegram,” Journal of Information Technology and Computer Science (INTECOMS), vol. 6, no. 2, pp. 1282-1288, 2023, doi: 10.31539/intercoms.v6i2.8284
Ni Made Dina Aprilianti, Jeanitha Gein, dkk, “Analisis Perbandingan Algoritma KNN, Gaussian Naïve Bayes, Random Forest Untuk Data Tidak Seimbang dan Data yang Diseimbangkan Dengan Metode Tomek Link Undersampling pada Dataset LCMS Tanaman Keladi Tikus,” Prosiding Sains Nasional dan Teknologi, vol. 13, no. 1, pp. 156-160, 2023, doi: 10.36499/psnst.v13i1.9002
Downloads
Additional Files
Posted
License
Copyright (c) 2025 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
