Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang
Keywords:
Random Forest, TF-IDF, Analisis sentiment, Support Vector Machine, Naive BayesAbstract
The growth of e-commerce in Indonesia has led to the emergence of various online shopping platforms, with Shopee being one of the most popular in Semarang City. User reviews on the Shopee application serve as a valuable data source for analyzing customer satisfaction levels; however, the large volume of data requires a systematic and accurate analytical approach. This study aims to analyze user review sentiments of the Shopee application using three machine learning algorithms: Random Forest, Naïve Bayes, and Support Vector Machine (SVM), as well as comparing the accuracy of these three algorithms. This research utilized 1000 reviews collected through web scraping from the Play Store, which were categorized into three classifications: positive, neutral, and negative sentiments. The analysis process encompassed pre-processing stages, feature extraction using TF-IDF, and classification using Random Forest, Naïve Bayes, and Support Vector Machine algorithms. The results demonstrated that the Random Forest algorithm achieved the highest accuracy at 96.19%, followed by Support Vector Machine with 95.71% accuracy, and Naïve Bayes with 84.76% accuracy. This research highlights the effectiveness of Random Forest and SVM in classifying user review sentiments towards the Shopee application.
References
Artanti, D. P., Syukur, A., Prihandono, A., & Setiadi, D. R. I. M. (2018). Analisa Sentimen Untuk Penilaian Pelayanan Situs Belanja Online Menggunakan Algoritma Naïve Bayes. 8–9.
Bailey, P., Craswell, N., & Hawking, D. (2003). Engineering a multi-purpose test collection for Web retrieval experiments. Information Processing and Management, 39(6), 853–871. https://doi.org/10.1016/S0306-4573(02)00084-5
Breiman, L. (2001). No Title. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
Cahyaningtyas, C., Nataliani, Y., & Widiasari, I. R. (2021). Analisis Sentimen Pada Rating Aplikasi Shopee Menggunakan Metode Decision Tree Berbasis SMOTE. Aiti, 18(2), 173–184. https://doi.org/10.24246/aiti.v18i2.173-184
Chiu, D. K. Y. (2001). BOOK REVIEW: “PATTERN CLASSIFICATION”, R. O. DUDA, P. E. HART and D. G. STORK, Second Edition. International Journal of Computational Intelligence and Applications, 01(03), 335–339. https://doi.org/10.1142/s1469026801000251
Hierons, R. (1999). Machine learning. Tom M. Mitchell. Published by McGraw-Hill, Maidenhead, U.K., International Student Edition, 1997. ISBN: 0-07-115467-1, 414 pages. Price: U.K. £22.99, soft cover. Software Testing, Verification and Reliability, 9(3), 191–193. https://doi.org/10.1002/(sici)1099-1689(199909)9:3<191::aid-stvr184>3.0.co;2-e
Ji, Y., Yu, S., & Zhang, Y. (2011). A novel Naive Bayes model: Packaged Hidden Naive Bayes. In 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference (pp. 484–487). IEEE. https://doi.org/10.1109/itaic.2011.6030379
Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. In Lecture Notes in Computer Science (pp. 137–142). Springer Berlin Heidelberg. https://doi.org/10.1007/bfb0026683
Josua Josen A. Limbong, Irwan Sembiring, & Kristoko Dwi Hartomo. (2022). Analisis Klasifikasi Sentimen Ulasan pada E-Commerce Shopee Berbasis Word Cloud Dengan Metode Naive Bayes dan K-Nearest Neighbor. Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(2), 347–356. https://doi.org/10.25126/jtiik.202294960
Khalaf, R., & Diana, A. (2022). Perancangan Sistem E-Commerce Dengan Menggunakan Business Model Canvas (BMC) Untuk Penjualan Pakaian Pada Exsthrift_. Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI), September, 161–170.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.2200/s00416ed1v01y201204hlt016
Liu, B. (2022). Sentiment analysis and opinion mining. books.google.com. https://books.google.com/books?hl=en&lr=&id=xYhyEAAAQBAJ&oi=fnd&pg=PP1&dq=sentiment+analysis+text+mining&ots=rk_wPDN2Cz&sig=HuNSIv63bnJHRPznM0RkJ456gDo
Lukmana, D. T., Subanti, S., & Susanti, Y. (2019). Analisis Sentimen Terhadap Calon Presiden 2019 Dengan Support Vector Machine Di Twitter. Seminar Nasional Penelitian Pendidikan Matematika (SNP2M) 2019 UMT, 2002, 154–160.
Manning, C. D. (2006). Natural Language Processing, Statistical Approaches to. In Encyclopedia of Cognitive Science. Wiley. https://doi.org/10.1002/0470018860.s00080
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - EMNLP ’02. https://doi.org/10.3115/1118693.1118704
Rahayu, A. S., Fauzi, A., & Rahmat, R. (2022). Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify. Jurnal Sistem Komputer Dan Informatika (JSON), 4(2), 349. https://doi.org/10.30865/json.v4i2.5398
Rahayu, I. P., Fauzi, A., & Indra, J. (2022). Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine. Jurnal Sistem Komputer Dan Informatika (JSON), 4(2), 296. https://doi.org/10.30865/json.v4i2.5381
Salton, G., Singhal, A., Mitra, M., & Buckley, C. (1997). Automatic text structuring and summarization. Information Processing & Management, 33(2), 193–207. https://doi.org/10.1016/s0306-4573(96)00062-3
Sari, F. V., & Wibowo, A. (2019). Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi. Jurnal SIMETRIS, 10(2), 681–686. https://jurnal.umk.ac.id/index.php/simet/article/view/3487/1883
SETIADI, K. (2022). Analisis Sentimen Pelanggan Terhadap Layanan ShopeeFood Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes dan Support Vector Machine (SVM). https://repository.mercubuana.ac.id/69948/%0Ahttps://repository.mercubuana.ac.id/69948/2/01 Cover.pdf
Tsoumakas, G., & Katakis, I. (2007). Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining, 3(3), 1–13. https://doi.org/10.4018/jdwm.2007070101
Utami, D. S., & Erfina, A. (2021). Analisis Sentimen Pinjaman Online di Twitter Menggunakan Algoritma Support Vector Machine (SVM). SISMATIK (Seminar Nasional Sistem Informasi Dan Manajemen Informatika), 1(1), 299–305.
Wagner, W. (2010). Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit: O’Reilly Media, Beijing, 2009, ISBN 978-0-596-51649-9. Language Resources and Evaluation, 44(4), 421–424. https://doi.org/10.1007/s10579-010-9124-x
Wetteland, R., Engan, K., Eftestøl, T., Kvikstad, V., & Janssen, E. (2019). Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks. Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. https://doi.org/10.5220/0007253603200327
Widagdo, A. S., W.A, B. S., & Nasiri, A. (2020). Analisis Tingkat Kepopuleran E-Commerce Di Indonesia Berdasarkan Sentimen Sosial Media Menggunakan Metode Naïve Bayes. Jurnal Informa : Jurnal Penelitian Dan Pengabdian Masyarakat, 6(1), 1–5. https://doi.org/10.46808/informa.v6i1.159
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z.-H., Steinbach, M., Hand, D. J., & Steinberg, D. (2007). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37. https://doi.org/10.1007/s10115-007-0114-2
Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. … Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1253
