Analisis Sentimen Produk Hijab Pada E-Commerce Tokopedia Menggunakan Algoritma Support Vector Machine dan IndoBERT Embedding
Kata Kunci:
IndoBERT, Support Vector Machine, Analisis Sentimen, Pemrosesan Bahasa AlamiAbstrak
Perkembangan teknologi informasi dan komunikasi telah membawa perubahan signifikan dalam dunia perdagangan, termasuk dalam platform e-commerce seperti Tokopedia. Salah satu sektor yang berkembang pesat adalah industri fesyen muslim, dengan produk hijab sebagai salah satu kategori yang paling diminati. Ulasan produk di platform e-commerce memainkan peran penting dalam pengambilan keputusan pembelian konsumen. Oleh karena itu, analisis sentimen terhadap ulasan produk hijab di Tokopedia menjadi penting untuk membantu penjual meningkatkan kualitas produk dan layanan. Penelitian ini bertujuan untuk merancang dan menguji model analisis sentimen menggunakan kombinasi IndoBERT embedding dan algoritma Support Vector Machine (SVM). IndoBERT digunakan untuk menghasilkan representasi teks yang mendalam berdasarkan konteks bahasa Indonesia, sementara SVM berfungsi sebagai algoritma klasifikasi untuk memprediksi sentimen ulasan. Untuk meningkatkan efisiensi komputasi, penelitian ini menggunakan metode ekstraksi embedding dengan menjumlahkan dua layer terakhir dari representasi model IndoBERT. Hasil penelitian menunjukkan bahwa kombinasi IndoBERT dan SVM memberikan performa yang baik dalam analisis sentimen. Kernel Linear mencapai akurasi tertinggi sebesar 92%. Kernel RBF memperlihatkan stabilitas tinggi dengan akurasi konsisten sebesar 89%, dan kernel Polynomial mencapai akurasi terbaik sebesar 90%. Penelitian ini menegaskan pentingnya pemilihan parameter yang tepat, seperti kernel, nilai C, dan class weight, untuk memaksimalkan performa model. Dengan demikian, kombinasi IndoBERT embedding dan algoritma SVM terbukti efektif dan efisien untuk analisis sentimen ulasan produk hijab di Tokopedia. Pendekatan ini dapat diterapkan pada berbagai domain lain untuk mendukung pengambilan keputusan berbasis data teks.
Referensi
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Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. O'Reilly Media.
Salton, G., & McGill, M. J. (1986). Introduction to modern information retrieval. McGraw-Hill.
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Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171-4186). https://doi.org/10.18653/v1/N19-1423
Koto, F., Lau, J. H., & Baldwin, T. (2020). IndoBERTweet: A pretrained language model for Indonesian Twitter with effective domain-specific vocabulary initialization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings (pp. 463-471). https://doi.org/10.18653/v1/2021.emnlp-main.833
Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press. https://doi.org/10.1017/CBO9780511801389
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press. https://doi.org/10.7551/mitpress/4175.001.0001
Vapnik, V. (1998). Statistical learning theory. Wiley-Interscience.
Katadata. (2021). Market size of Indonesia's hijab industry reaches Rp 33 trillion.
Pratama, M. R., Soerawinata, F. A., Zhafari, R. R., Rendy, & Imanda, H. N. (2022). Sentiment analysis of beauty product e-commerce using Support Vector Machine method. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi, 6(2), 123-132. https://doi.org/10.29207/resti.v6i2.3876
Sitepu, M. B., Munthe, I. R., & Harahap, S. Z. (2022). Implementation of Support Vector Machine algorithm for Shopee customer sentiment analysis. Sinkron, 7(2), 619-634. https://doi.org/10.33395/sinkron.v7i2.11408
Hoang, M., Bihorac, O. A., & Rouces, J. (2019). Aspect-based sentiment analysis using bert. In Proceedings of the 22nd Nordic Conference on Computational Linguistics (pp. 187-196).
Mustakim, H., & Priyanta, S. (2022). Aspect-based sentiment analysis of KAI Access reviews using NBC and SVM. Indonesian Journal of Computing and Cybernetics Systems, 7(2), 619-634. https://doi.org/10.22146/ijccs.68903
Al-Smadi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., & Gupta, B. (2018). Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews. Journal of Computational Science, 27, 386-393. https://doi.org/10.1016/j.jocs.2017.11.006
Albu, I. A., & Spînu, S. (2022). Emotion detection from tweets using a BERT and SVM ensemble model. arXiv preprint arXiv:2208.04547.
globaleyez. (2023). What is Tokopedia. globaleyez.
Wahyuni, S. (2020). The impact of e-commerce on small and medium-sized enterprises (SMEs) in Indonesia: A case study of Tokopedia. Journal of Business and Management, 22(1), 1-12.
Kurniawan, A. (2020). The effect of online shopping experience on customer satisfaction and loyalty: A study on Tokopedia. International Journal of Business and Management, 15(2), 1-10.
Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers. http://dx.doi.org/10.2200/S00416ED1V01Y201204HLT016
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. https://doi.org/10.1561/1500000011
Yang, I.-T., & Prayogo, H. (2022). Efficient reliability analysis of structures using symbiotic organisms search-based active learning support vector machine. Buildings, 12(4), 455. https://doi.org/10.3390/buildings12040455
Jurafsky, D., & Martin, J. H. (2019). Speech and language processing. https://web.stanford.edu/~jurafsky/slp3/old_oct19/17.pdf
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. O'Reilly Media.
Salton, G., & McGill, M. J. (1986). Introduction to modern information retrieval. McGraw-Hill.
Porter, M. F. (1980). An algorithm for suffix stripping. Program: Electronic Library and Information Systems, 14(3), 130-137. https://doi.org/10.1108/eb046814
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171-4186). https://doi.org/10.18653/v1/N19-1423
Koto, F., Lau, J. H., & Baldwin, T. (2020). IndoBERTweet: A pretrained language model for Indonesian Twitter with effective domain-specific vocabulary initialization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings (pp. 463-471). https://doi.org/10.18653/v1/2021.emnlp-main.833
Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press. https://doi.org/10.1017/CBO9780511801389
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press. https://doi.org/10.7551/mitpress/4175.001.0001
Vapnik, V. (1998). Statistical learning theory. Wiley-Interscience.
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