Optimasi Particle Swarm Optimization Pada Naive Bayes Untuk Sentiment Analysis Furniture
Abstract
Abstrak: Penelitian ini memfokuskan pada peningkatan akurasi produk furniture dengan menggunakan algoritma Naive Bayes yang dioptimasi PSO. Fungsi dari penerapan metode algoritma tersebut dalam mengklasifikasikan review positif dan negatif yaitu untuk memudahkan pembeli mendapatkan informasi secara mudah sebelum memilih produk yang tepat untuk dibeli. Pengolahan data uji training menggunakan 100 data review furniture positif dan 100 data review furniture negatif. Hasil eksperimen pada Naive Bayes optimasi Particle Swarm Optimization (PSO) sebesar 93,50% sehingga dapat disimpulkan bahwa Naive Bayes merupakan metode klasifikasi teks yang sederhana dan PSO salah satu teknik optimasi yang berfungsi untuk meningkatkan pembobotan atribut (atribute weight).Kata kunci: text mining, sentiment analisis, Naive Bayes, Naive Bayes optimasi PSO
Abstract: This study focuses on improving the accuracy of furniture products using the Naive Bayes algorithm optimized by PSO. The function of the application of the algorithm method is to classify positive and negative reviews that is to make it easier for buyers to get information easily before choosing the right product to buy. The training test data processing uses 100 positive furniture review data and 100 negative furniture review data. The experimental results on Naive Bayes Particle Swarm Optimization (PSO) optimization were 93.50% so that it can be concluded that Naive Bayes is a simple text classification method and PSO is one of the optimization techniques that serves to increase attribute weighting.
Keywords: text mining,sentiment analysis, Naive Bayes, PSO Optimization Naive Bayes
Published
2018-12-27
How to Cite
AULIANITA, Rizki; RIFAI, Achmad.
Optimasi Particle Swarm Optimization Pada Naive Bayes Untuk Sentiment Analysis Furniture.
INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management, [S.l.], v. 3, n. 1, p. 31-40, dec. 2018.
ISSN 2548-3331.
Available at: <http://ejournal-binainsani.ac.id/index.php/IMBI/article/view/1043>. Date accessed: 06 sep. 2024.
Section
Articles