PERBANDINGAN KINERJA KLASIFIKASI SENTIMEN ULASAN PRODUK PEMBELIAN BERAS DI MARKETPLACE SHOPEE

Authors

  • Dedy Sugiarto Universitas Trisakti
  • Syandra Sari
  • Anung Barlianto Ariwibowo
  • Fitria Nabilah Putri
  • Dimmas Mulya
  • Tasya Aulia
  • Arviandri Naufal Zaki

Keywords:

rice, product review, sentiment analysis, support vector machine, naive bayes, logistic regression, k-nearest neighbor, TF-IDF, bag of word

Abstract

This study aims to compare the performance of product purchase sentiment classification in market place shopee using four classification algorithms, namely support vector machine (SVM), naïve bayes (NB), logistic regression (LR),  k-nearest neighbor (KNN) and associated with the feature extraction model used, namely term frequency - inverse document. frequency (TF-IDF) and bag of word (BOW).   Data collection was carried out by extracting rice product review data through the Shopee website using a web scraping technique which was then saved in the form of a file with CSV format. The number of product reviews obtained is 3531 reviews and after pre-processing through the elimination of duplicate reviews, there are 464 reviews with details 16.17% having a negative label (rating 1 or 2), 15.52% having a neutral label (rating 3), and 68.32% have a positive label (rating 4 or 5). The composition of the rankings shows that the data is not balanced. The experimental results show that the combination of LR with TF-IDF shows the best performance with an accuracy of 80%.

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References

A. N. Ardianti and M. A. Widiartanto, “Pengaruh Online Customer Review dan Online Customer Rating terhadap Keputusan Pembelian melalui Marketplace Shopee .,” J. Ilmu Adm. Bisnis, pp. 1–11, 2019.

R. S. Damayanti, “Pengaruh Online Costumer Review and Rating, E-Service Quality dan Price Terhadap Minat Beli pada Online Marketplace (Studi Empiris Pada Mahasiswa Universitas Muhammadyah Magelang),” Pros. 2nd Bus. Econ. Conf. Util. Mod., pp. 684–693, 2019, [Online]. Available: http://journal.ummgl.ac.id/index.php/conference/article/download/3559/1731.

Y. Basani, H. V. Sibuea, S. Ida Patona Sianipar, and J. Presly Samosir, “Application of Sentiment Analysis on Product Review E-Commerce,” J. Phys. Conf. Ser., vol. 1175, no. 1, 2019, doi: 10.1088/1742-6596/1175/1/012103.

X. Fang and J. Zhan, “Sentiment analysis using product review data,” J. Big Data, vol. 2, no. 1, 2015, doi: 10.1186/s40537-015-0015-2.

L. O. Sihombing, H. Hannie, and B. A. Dermawan, “Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier,” Edumatic J. Pendidik. Inform., vol. 5, no. 2, pp. 233–242, 2021, doi: 10.29408/edumatic.v5i2.4089.

Y. R. Saputri and H. Februariyanti, “Jurnal Mantik SENTIMENT ANALYSIS ON SHOPEE E-COMMERCE USING THE,” vol. 6, no. 36, pp. 1349–1357, 2022.

R. Kosasih and A. Alberto, “Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier,” Ilk. J. Ilm., vol. 13, no. 2, pp. 101–109, 2021, doi: 10.33096/ilkom.v13i2.721.101-109.

T. Hariguna, W. M. Baihaqi, and A. Nurwanti, “Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm,” IJIIS Int. J. Informatics Inf. Syst., vol. 2, no. 2, pp. 48–55, 2019, doi: 10.47738/ijiis.v2i2.13.

J. Novák, P. Benda, E. Šilerová, J. Vaněk, and E. Kánská, “Sentiment Analysis in Agriculture,” Agris On-line Pap. Econ. Informatics, vol. 13, no. 1, pp. 121–130, 2021, doi: 10.7160/aol.2021.130109.

J. Sen et al., Machine Learning: Algorithms, Models, and Applications, no. January. 2022.

S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1–16, 2019, doi: 10.1186/s12911-019-1004-8.

S. Uddin, I. Haque, H. Lu, M. A. Moni, and E. Gide, “Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction,” Sci. Rep., vol. 12, no. 1, pp. 1–11, 2022, doi: 10.1038/s41598-022-10358-x.

T. Sabri, O. El Beggar, and M. Kissi, “Comparative study of Arabic text classification using feature vectorization methods,” Procedia Comput. Sci., vol. 198, no. 2021, pp. 269–275, 2021, doi: 10.1016/j.procs.2021.12.239.

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Published

2023-01-30