Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier

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.Mengetahui tingkat kepuasan dan kepercayaan customer merupakan salah satu kunci kesuksesan dalam sebuah bisnis. Namun, membaca review dari para customer secara manual dinilai tidak efektif mengingat banyaknya data yang masuk ke situs Shopee setiap harinya. Untuk mengatasi masalah tersebut, analisis sentimen diterapkan untuk melakukan analisis review secara otomatis. Analisis sentimen pada penelitian ini dilakukan dengan menerapkan algoritma Naive Bayes Classifier sebagai model klasifikasi. Naive Bayes Classifier merupakan salah satu algoritma supervised learning yang populer karena dinilai cepat dan mudah dalam pengaplikasiannya. Hasil penelitian dengan menggunakan algoritma Naive Bayes Classifier mendapat nilai akurasi sebesar 85% dengan total masing-masing sebanyak 2750 review berlabel positive, 65 review berlabel neutral, 134 review berlabel negative dan 436 review yang tidak terklasifikasi dengan sempurna atau misclassified data