Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis
DOI:
10.33395/sinkron.v7i2.11408Abstract
As the number one largest marketplace in Indonesia based on the criteria for the origin of international stores, Shopee must always improve the quality of its products and services based on reviews from users. Given the huge number of user reviews, it is not effective to identify them by reading one by one. For this reason, an automated system is needed that can read and identify reviews better. Sentiment analysis has proven to do the job. This study aims to conduct a sentiment analysis of shopee product reviews from users who use English. This study applies the Support Vector Machine algorithm to classify the Shopee user review data. To solve this problem, the research was carried out by going through several stages, namely: pre-processing the text of the dataset, performing feature extraction, after that the word weighting was carried out using the TF-IDF method, after clean data was obtained, the SVM algorithm was implemented, for further evaluation of the model. In the results of the study, it was found that the word that most represented the positive opinion of Shopee customers was "Good" with a total of 4684 words. While the word that represents the most negative opinion is "Seller" with 68 words. From the five sentiment analysis models tested, the average value of the confusion matrix is obtained, which are precision=1, recall=0.97, and f1-score=0.98. From this research, it can be concluded that the SVM algorithm is proven to be applicable in conducting sentiment analysis on user reviews of Shopee products with an average accuracy rate of 97.3%.
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Azzahro, F., Handayani, P. W., Murti, S. S., & Yudhoatmojo, S. B. (2020). The Effects of Perceived Justice and Emotions on Service Recovery Satisfaction on Indonesian B2B and C2C E-commerce Customers. Jurnal Sistem Informasi, 16(1), 38–48.
Hafidz, N., & Yanti Liliana, D. (2021). Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 213–219. https://doi.org/10.29207/resti.v5i2.2960
Hariguna, T., Baihaqi, W. M., & Nurwanti, A. (2019). Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm. International Journal of Informatics and Information Systems, 2(2), 48–55. https://doi.org/10.47738/ijiis.v2i2.13
Iprice. (2021). Peta E-Commerce Indonesia. Retrieved April 18, 2022, from iprice.co.id website: https://iprice.co.id/insights/mapofecommerce/
Iskandar, J. W., & Nataliani, Y. (2021). Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(6), 1120–1126. https://doi.org/10.29207/resti.v5i6.3588
Kabiru, I. N., & Sari, P. K. (2019). Analisa Konten Media Sosial E-Commerce Pada Instagram Menggunakan Metode Sentimen Analysis Dan LDA-Based Topic Modeling (Studi Kasus : Shopee Indonesia). E-Proceeding of Management, 6(1), 12–19.
Kurniawan, S., Gata, W., Puspitawati, D. A., Nurmalasari, Tabrani, M., & Novel, K. (2019). Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada Komentar Media Berita Online. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(2), 176–183. https://doi.org/10.29207/resti.v3i2.935
Limbong, J. J. A., Sembiring, I., & Hartomo, K. D. (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 (JTIIK), 9(2), 347–356. https://doi.org/10.25126/jtiik.202294960
Muktafin, E. H., Kusrini, K., & Luthfi, E. T. (2020). Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing. Jurnal Eksplora Informatika, 10(1), 32–42. https://doi.org/10.30864/eksplora.v10i1.390
Negara, A. B. P., Muhardi, H., & Putri, I. M. (2020). Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 599. https://doi.org/10.25126/jtiik.2020711947
Nurdin, Hutomi, M., Qamal, M., & Bustami, B. (2020). Sistem Pengecekan Toko Online Asli atau Dropship pada Shopee Menggunakan Algoritma Breadth First Search. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(6), 1117–1123. https://doi.org/10.29207/resti.v4i6.2514
Pratmanto, D., Rousyati, R., Wati, F. F., Widodo, A. E., Suleman, S., & Wijianto, R. (2020). App Review Sentiment Analysis Shopee Application in Google Play Store Using Naive Bayes Algorithm. Journal of Physics: Conference Series, 1641(1). https://doi.org/10.1088/1742-6596/1641/1/012043
Rahat, A. M., Kahir, A., & Masum, A. K. M. (2019). Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset. 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), 266–270. https://doi.org/10.1109/SMART46866.2019.9117512
Rhohmawati, U., Slamet, I., & Pratiwi, H. (2019). Sentiment Analysis Using Maximum Entropy on Application Reviews (Study Case: Shopee on Google Play). Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), 5(1), 44–49. https://doi.org/10.26555/jiteki.v5i1.13087
Rolliawati, D., Khalid, K., & Rozas, I. S. (2020). Teknologi Opinion Mining untuk Mendukung Strategic Planning. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(2), 293. https://doi.org/10.25126/jtiik.2020721685
Saputra, F. T., Nurhadryani, Y., Wijaya, S. H., & Defina, D. (2021). Analisis Sentimen Bahasa Indonesia pada Twitter Menggunakan Struktur Tree Berbasis Leksikon. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(1), 135. https://doi.org/10.25126/jtiik.0814133
Sihombing, L. O., Hannie, H., & Dermawan, B. A. (2021). Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier. Edumatic: Jurnal Pendidikan Informatika, 5(2), 233–242. https://doi.org/10.29408/edumatic.v5i2.4089
Siswanto, Wibawa, Y. P., Gata, W., Gata, G., & Kusumawardhani, N. (2018). Classification Analysis of MotoGP Comments on Media Social Twitter Using Algorithm Support Vector Machine and Naive Bayes. 2018 International Conference on Applied Information Technology and Innovation (ICAITI), 96–101. https://doi.org/10.1109/ICAITI.2018.8686751
Somantri, O., & Apriliani, D. (2018). Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 5(5), 537–548. https://doi.org/10.25126/jtiik.201855867
Widyastuti, H., & Prastitya, T. A. (2020). Preferensi Konsumen Pengguna E-Commerce yang Memengaruhi Kesadaran akan Perlindungan Konsumen pada Generasi X. Jurnal Sistem Informasi Bisnis, 10(1), 10–19. https://doi.org/10.21456/vol10iss1pp10-19
Wiratama, G. P., & Rusli, A. (2019). Sentiment Analysis of Application User Feedback in Bahasa Indonesia Using Multinomial Naive Bayes. 2019 5th International Conference on New Media Studies (CONMEDIA), 223–227. https://doi.org/10.1109/CONMEDIA46929.2019.8981850
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Copyright (c) 2022 Melda Betaria Sitepu, Ibnu Rasyid Munthe, Syaiful Zuhri Harahap
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Muhammad Khoiruddin harahap
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