Optimalisasi Model Klasifikasi Sentimen Netizen Terhadap Merek Tas Luar Negeri

Mochamad Nurul Huda; Daffa Almer Fauzan; Muhammad Raihan Satrio Putra  Pamungkas; Nikita Sabila  Ratnadewi; Azzahra Ayu  Vahendra

doi:10.35134/komtekinfo.v10i1.360

Authors

Mochamad Nurul Huda Universitas Pendidikan Indonesia
Daffa Almer Fauzan Universitas Pendidikan Indonesia
Muhammad Raihan Satrio Putra Pamungkas Universitas Pendidikan Indonesia
Nikita Sabila Ratnadewi Universitas Pendidikan Indonesia
Azzahra Ayu Vahendra Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.35134/komtekinfo.v10i1.360

Keywords:

Sentiment Analysis, Brand, Machine Learning, Classification, SMOTE, Sentimen Analisis, Merek, Pembelajaran Mesin, Klasifikasi

Abstract

Research on text mining has grown more than ever in various sectors. Public figures have also grown in interest towards the field and have the tendency to get to know more about consumers’ perceptions toward relevant goods and the reputation of an individual in social media. Sentiment analysis is a state-of-the-art technique that can be utilized to evaluate such trends or general views, for instance the reputation of a fashion brand. The dataset is built upon the crawled tweets that are relevant with the required topics which have the purpose to analyze the preferred fashion brand of the public. This study shows that the public leads to a positive notion toward foreign bag brands. The algorithms that are being compared includes Logistic Regression, Multinomial Naïve Bayes, Decision Tree, K-Nearest Neighbors, Random Forest, and Support Vector Machine. Support Vector Machine provides the best model which reaches 69% in accuracy. The Synthetic Minority Oversampling Technique (SMOTE) was also conducted to improve the model. Result shows that the Support Vector Machine model has successfully increased its accuracy by 13%, reaching an accuracy of 82%.

Keywords: Sentiment Analysis, Brand, Machine Learning, Classification, SMOTE

Abstrak

Penelitian mengenai text mining telah mengalami peningkatan dibanding sebelumnya di dalam berbagai sektor. Figur publik juga semakin tertarik terhadap bidang tersebut dan memiliki kecenderungan untuk mengetahui lebih banyak mengenai persepsi konsumen terhadap suatu barang dan mengenai reputasi seseorang di media sosial. Sentimen analisis merupakan sebuah teknik state-of-the-art yang dapat digunakan untuk mengevaluasi suatu tren atau pandangan umum mengenai suatu hal, misalnya reputasi sebuah merek fashion. Sumber himpunan data yang digunakan pada penelitian ini dibuat berdasarkan crawling tweet yang relevan dengan topik yang dibutuhkan, yang bertujuan untuk menganalisis merek fashion yang disukai oleh masyarakat. Penelitian ini menunjukkan bahwa persepsi masyarakat mengarah pada persepsi positif terhadap merek tas luar negeri. Pada penelitian ini, beberapa algoritma digunakan sebagai perbandingan, antara lain Logistic Regression, Multinomial Naïve Bayes, Decision Tree, K-Nearest Neighbors, Random Forest, dan Support Vector Machine. Hasil pengujian model menunjukkan algoritma Support Vector Machine memiliki performa terbaik dengan accuracy sebesar 69%. Kemudian digunakan teknik Synthetic Minority Oversampling Technique (SMOTE) untuk meningkatkan performa dari model. Hasil menunjukkan bahwa model algoritma Support Vector Machine telah berhasil ditingkatkan dengan accuracy sebesar 13%, mencapai accuracy sebesar 82%.

Kata kunci: Sentimen Analisis, Merek, Pembelajaran Mesin, Klasifikasi, SMOTE

References

Hidapenta, D., & Dewi, D. A. (2021). Peran Pkn Mengatasi Fenomena Kecintaan Produk Luar Yang Terjadi Di Indonesia. Jurnal Kewarganegaraan, 5(1), 168–175. https://doi.org/10.31316/jk.v5i1.1401

Yanuarsari, D. H. (2015). Analisis Minat Beli Wanita Terhadap Produk Tas Bermerek Original di Tengah Komoditi Produksi Tas Bermerek Tiruan Produksi Produsen Lokal. ANDHARUPA: Jurnal Desain Komunikasi Visual & Multimedia, 1(02), 110–121. https://doi.org/10.33633/andharupa.v1i02.961

Vijay, S., Prashar, S., & Gupta, S. (2019). An examination of the role of review valence and review source in varying consumption contexts on purchase decision. Journal of Retailing and Consumer Services, 52(January), 101734. https://doi.org/10.1016/j.jretconser.2019.01.003

Chetioui, Y., Benlafqih, H., & Lebdaoui, H. (2020). How fashion influencers contribute Influencers to consumers ’ purchase intention. 24(3), 361–380. https://doi.org/10.1108/JFMM-08-2019-0157

Yaacob, A., Gan, J. L., & Yusuf, S. (2021). The Role of Online Consumer Review, Social Media Advertisement and Influencer Endorsement on Purchase Intention of Fashion Apparel during COVID-19. Journal of Content, Community & Communication, 14(8), 17–33. https://doi.org/10.31620/JCCC.12.21/03

Lee, M., Cai, Y. (Maggie), DeFranco, A., & Lee, J. (2020). Exploring influential factors affecting guest satisfaction: Big data and business analytics in consumer-generated reviews. Journal of Hospitality and Tourism Technology, 11(1), 137–153. https://doi.org/10.1108/JHTT-07-2018-0054

Weisstein, F. L., Song, L., Andersen, P., & Zhu, Y. (2017). Examining impacts of negative reviews and purchase goals on consumer purchase decision. Journal of Retailing and Consumer Services, 39(June), 201–207. https://doi.org/10.1016/j.jretconser.2017.08.015

Diekson, Z. A., Prakoso, M. R. B., Putra, M. S. Q., Syaputra, M. S. A. F., Achmad, S., & Sutoyo, R. (2023). Sentiment analysis for customer review: Case study of Traveloka. Procedia Computer Science, 216(2022), 682–690. https://doi.org/10.1016/j.procs.2022.12.184

Prananda, A. R., & Thalib, I. (2020). Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion. Journal of Information Systems Engineering and Business Intelligence, 6(1), 1. https://doi.org/10.20473/jisebi.6.1.1-8

Rambocas, M., & Pacheco, B. G. (2018). Online sentiment analysis in marketing research: a review. Journal of Research in Interactive Marketing, 12(2), 146–163. https://doi.org/10.1108/JRIM-05-2017-0030

Lee, D., Jo, J.-C., & Lim, H.-S. (2017). User Sentiment Analysis on Amazon Fashion Product Review Using Word Embedding. Journal of the Korea Convergence Society, 8(4), 1–8. https://doi.org/10.15207/jkcs.2017.8.4.001

Pantano, E., Giglio, S., & Dennis, C. (2019). Making sense of consumers’ tweets: Sentiment outcomes for fast fashion retailers through Big Data analytics. International Journal of Retail and Distribution Management, 47(9), 915–927. https://doi.org/10.1108/IJRDM-07-2018-0127

Susilayasa, I. M. A., Eka Karyawati, A. A. I., Astuti, L. G., Rahning Putri, L. A. A., Arta Wibawa, I. G., & Ari Mogi, I. K. (2022). Analisis Sentimen Ulasan E-Commerce Pakaian Berdasarkan Kategori dengan Algoritma Convolutional Neural Network. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), 11(1), 1. https://doi.org/10.24843/jlk.2022.v11.i01.p01

Muslim, M. A., Dasril, Y., Alamsyah, A., & Mustaqim, T. (2021). Bank predictions for prospective long-term deposit investors using machine learning LightGBM and SMOTE. Journal of Physics: Conference Series, 1918(4). https://doi.org/10.1088/1742-6596/1918/4/042143

Peffers, K., Tuunanen, T., Gengler, C. E., Rossi, M., Hui, W., Virtanen, V., & Bragge, J. (2020). Design Science Research Process: A Model for Producing and Presenting Information Systems Research. ArXiv, abs/2006.0.

Huda, M. N. (2023). Bag Brands Sentiment Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7679325

Lourdusamy, R., & Abraham, S. (2018). A Survey on Text Pre-processing Techniques and Tools. International Journal of Computer Sciences and Engineering, 06(03), 148–157. https://doi.org/10.26438/ijcse/v6si3.148157

Turki, T., & Roy, S. S. (2022). Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer. Applied Sciences (Switzerland), 12(13). https://doi.org/10.3390/app12136611

Pamungkas, M. R. S. P., Huda, M. N., Fauzan, D. A., Itsna, A. H., & Al-Hijri, F. M. (2022). Sistem Klasifikasi Otomatis Dengan Konsep Machine Learning As A Service ( MLaaS ) Pada Kasus Pesan Berindikasi Cyberbullying. ILKOMNIKA: Journal of Computer and Applied Informatics, 4(3), 252–261. https://doi.org/10.28926/ilkomnika.v4i3.522

Chawla, N. V., Bowyer, K. W., Hall, L. O., Department, & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research (JAIR), 16, 321–357. https://doi.org/10.1613/jair.953

Alkharabsheh, K., Alawadi, S., Kebande, V. R., Crespo, Y., Fernández-Delgado, M., & Taboada, J. A. (2022). A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class. Information and Software Technology, 143, 106736. https://doi.org/10.1016/j.infsof.2021.106736

Kabir, A. I., Ahmed, K., & Karim, R. (2020). Word Cloud and Sentiment Analysis of Amazon Earphones Reviews with R Programming Language. Informatica Economica, 24(4/2020), 55–71. https://doi.org/10.24818/issn14531305/24.4.2020.05