Detect Fake Reviews Using Random Forest and Support Vector Machine

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

Deteksi Fake Review Menggunakan Support Vector Machine

Maraknya berbagai e-commerce menjadikan calon pembeli semakin selektif sehingga bergantung pada review yang ditinggalkan oleh pembeli sebelumnya untuk menentukan keputusan membeli suatu produk. Banyaknya review, baik itu yang bersifat positif atau negatif, sangat mempengaruhi sisi mana yang dapat dipercaya. Jika review yang dibaca tidak nyata atau disebut fake review maka akan merugikan baik sisi penjual ataupun sisi pembeli. Untuk itu, perlu dilakukan analisis untuk mendeteksi fake review pada kumpulan review produk. Penelitian ini dilakukan dengan pendekatan lima kelas feature yaitu sentiment feature, personal feature, brand-only feature, content feature, dan metadata feature dengan menggunakan metode klasifikasi Support Vector Machine. Pada penelitian ini dibandingkan antara SentiwordNet dan SenticNet untuk mendapatkan ekstraksi sentiment mana yang lebih baik. Pada penelitian ini juga dilakukan pemilihan dan penggabungan feature, serta tuning parameter dan jenis kernel pada SVM apakah akan memengaruhi sistem. Hasil terbaik diperoleh akurasi sebesar 74,46%. Dari hasil penelitian ini diperoleh bahwa SenticNet lebih baik daripada SentiwordNet, kemudian tuning parameter serta pemilihan jenis kernel pada SVM bisa mendapatkan hasil yang optimal, serta penggunaan sentiment feature sangat mempengaruhi sistem untuk deteksi fake review