Detect Fake Reviews Using Random Forest and Support Vector Machine
DOI:
10.33395/sinkron.v8i2.12090Keywords:
Tokopedia, Fake Review, Pos Tagging, Support Vector Machine, Random Forest.Abstract
With the rapid development of e-commerce, which makes it possible to
buy and sell products and services online, customers are increasingly using these
online shop sites to fulfill their needs. After purchase, customers write reviews
about their personal experiences, feelings and emotions. Reviews of a product are
the main source of information for customers to make decisions to buy or not a
product. However, reviews that should be one piece of information that can be
trusted by customers can actually be manipulated by the owner of the seller. Where
sellers can spam reviews to increase their product ratings or bring down their
competitors. Therefore this study discusses detecting fake reviews on product
reviews on Tokopedia. Where the method used is the distribution post tagging
feature to perform detection. By using the post tagging feature method the
distribution got 856 fake reviews and 4478 genuine reviews. In the fake reviews,
there were 628 reviews written with the aim of increasing product sales or brand
names from store owners, while there were 228 reviews aimed at dropping their
competitors or competitors. Furthermore, the classification is carried out using the
random forest algorithm model and the support vector machine. By dividing the
dataset for training data by 80% while 20% for data testing. Here it is known that
the support vector machine gets much higher accuracy than the random forest. The
support vector machine gets an accuracy of 98% while the random forest gets an
accuracy of 60%
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Copyright (c) 2023 Zulpan Hadi, Ema Utami, Dhani Ariatmanto
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
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