Analisis Sentimen Terhadap Penilaian Customer Di PHD Karawitan Menggunakan Metode K-Nearest Neighbor

PHD (Pizza hut delivery) menyediakan wadah untuk berbagi pengalaman customer berupa penilaian terhadap outlet PHD yang dikunjunginya yang dapat disalurkan melalui website www.halopizzaindonesia.com. Opini yang masuk akan didistribusikan ke masing-masing outlet PHD termasuk outlet PHD Karawitan. Outlet Manager ditugaskan untuk menghitung persentase penilaian kepuasan customer dengan cara mempelajari opini yang disampaikan oleh customer dan mengelompokkan penilaian kedalam beberapa klasifikasi yaitu opini puas dan opini tidak puas. Jumlah penilaian yang banyak membuat outlet manager mengalami kesulitan dikarenakan harus mengelompokkan opini satu persatu dari sekian banyaknya opini yang ada lalu menghitung persentase kepuasan customer secara manual. Untuk mempermudah pekerjaan outlet manager terutama khusus dalam pengelompokkan penilaian berupa opini customer, maka diperlukan suatu metode yang dapat menganalisis opini secara otomatis. Salah satu cara yang dapat digunakan untuk mengelompokan opini yaitu dengan menggunakan metode analisis sentimen. Metode analisis sentimen yang di gunakan dalam penelitian ini yaitu menggunakan algoritma K-nearest neigbor. Tahapannya terdiri dari preprocessing, pembobotan kata, dan klasifikasi k-nearest neigbor. Berdasarkan hasil implementasi dan pengujian yang telah dilakukan algoritma k-nearest neigbor dapat diterapkan pada kasus penelitian customer di PHD Karawitan dan dapat membantu mempermudah outlet manager dengan pengelompokkan penilaian opini puas dan opini tidak puas secara otomatis serta dapat menghitung persentase opini tersebut.

PHD (Pizza hut delivery) provides a forum to share their experiences in the form of an assessment of the customers who visited the outlet PHD can be routed via the website www.halopizzaindonesia.com. Opinion received will be distributed to each outlet including an outlet PHD PHD Karawitan. Outlet Manager assigned to calculate the percentage of customer satisfaction ratings by studying the opinion delivered by the customer and group assessment into several classifications are satisfied opinion and opinions are not satisfied. Number of ratings that many managers have difficulty making outlets due to be classifying opinions one by one of the many opinions there and then calculate the percentage of customer satisfaction manually. To facilitate the work, especially outlet manager specialized in grouping assessment such as customer opinion, we need a method that can automatically analyze opinion. One way that can be used to categorize opinion by using sentiment analysis method. Sentiment analysis method used in this research that uses the K-nearest algorithm neigbor. Consists of a preprocessing stage, weighting word, and classification k-nearest neigbor. Based on the results of the implementation and testing that has been performed k-nearest neigbor algorithm can be applied to a case study in PHD Karawitan customer and can help simplify the outlet manager satisfied with the grouping opinion ratings and opinions are not satisfied automatically, and can calculate the percentage of the opinion.