Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes

Authors

  • Rahmawan Bagus Trianto Universitas An Nuur
  • Andri Triyono Universitas An Nuur
  • Dhika Malita Puspita Arum Universitas An Nuur

DOI:

https://doi.org/10.32493/informatika.v5i3.6110

Keywords:

Electronic product review, Classification, Naïve Bayes, n-gram, TF-IDF

Abstract

Online product ratings usually provide descriptive reviews and also reviews in the form of ratings. Likewise, what was done at the Lazada online store. Descriptive review can provide a clear view compared to a rating review to other potential buyers. However, in reality there is a mismatch between the description review and the rating given. This creates a lack of information for sellers as well as potential buyers. Automatic classification of buyer descriptive reviews is proposed in this study so that there is a match between descriptive reviews and rating reviews. This automatic classification descriptive review uses the Naive Bayes algorithm with n-gram feature extraction and TF-IDF word weighting. The results of this study obtained the best accuracy of 94.06%, a recall of 91.73% and precision of 90.71% in Bigram feature extraction. With this accuracy value it can be used as a reference or model for classifying product description reviews, so that the feedback process between sellers and buyers can run well.

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Published

2020-09-30

Issue

Vol. 5 No. 3 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

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