Year-end Sale % OFF 
Abstract
Gaining customer satisfaction and trust has become the main challenge in achieving success in the business world. Business people need to identify problems that arise from reviews given by customers. However, reading and classifying each review takes a long time and is considered ineffective. To overcome this, this study aims to analyze the customer sentiment of shopee products using the nave Bayes classifier algorithm. The data used in this study is a customer review of the Xiaomi Redmi Note 9 products which are sold on the Shopee Indonesia website. Customer review data is collected by applying the Web Scraping technique. The algorithm used in this study is the Naïve Bayes Classifier which is known to be popular and effective in classifying data. This study also applies the Knowledge Discovery in Text (KDT) methodology to extract information from text data. The results of the classification using the Naïve Bayes algorithm found an accuracy value of 85%. This study proves that by applying sentiment analysis techniques, business people are able to find out the opinions of customers as an evaluation material that needs to be done to optimize the products and services provided.
Highlights
PENDAHULUAN Kegiatan jual beli barang dan jasa secara online atau yang biasa disebut dengan ecommerce telah berkembang pesat saat ini
To overcome this, this study aims to analyze the customer sentiment of shopee products using the nave Bayes classifier algorithm
This study proves that by applying sentiment analysis techniques, business people are able to find out the opinions of customers as an evaluation material that needs to be done to optimize the products and services provided
Summary
Skenario penelitian yang akan dilakukan pada penelitian ini diperlihatkan pada gambar 1 dengan menggunakan bantuan software Orange Data Mining. Data yang akan digunakan pada penelitian ini adalah data review salah satu produk smartphone dengan merek Xioami Redmi Note 9 yang dijual secara online di website Shopee Indonesia dan dikelola langsung oleh xiaomi.official.id. Pengambilan data dilakukan dengan menerapkan teknik web scraping menggunakan bahasa Python. Data mentah yang terkumpul berjumlah 3006 review yang kemudian dilakukan pemberian label atau labeling secara manual ke dalam beberapa kelas diantaranya positive, neutral, dan negative sesuai dengan sentimen kata yang berada pada review produk. Review yang tidak mengandung sentimen manapun dan tidak berkaitan dengan produk tersebut akan diberi label unrelated. Data dengan label unrelated kemudian dihapus sehingga menyisakan kelas positive, neutral, dan negative seperti yang terlihat pada tabel 1
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