Open AccessK-Nearest Neighbor Berbasis Particle Swarm Optimization untuk Analisis Sentimen Terhadap Tokopedia
Author(s) -
Dicki Pajri,
Yuyun Umaidah,
Tesa Nur Padilah
Publication year - 2020
Publication title -
jutisi (jurnal teknik informatika dan sistem informasi)
Language(s) - English
Resource type - Journals
ISSN - 2443-2229
DOI - 10.28932/jutisi.v6i2.2658
Subject(s) - particle swarm optimization , confusion matrix , confusion , perception , precision and recall , computer science , swarm behaviour , k nearest neighbors algorithm , artificial intelligence , mathematics , algorithm , pattern recognition (psychology) , psychology , neuroscience , psychoanalysis
Tokopedia is a popular marketplace used by e-commerce in Indonesia. Customers’ perception of Twitter towards Tokopedia can be used as an important source of information and can be processed into useful insights. Sentiment analysis is a solution that can be used to process the customers’ perception using K-Nearest Neighbor based on Particle Swarm Optimization. The purpose of this study is to classify customers’ perception based on positive, neutral, and negative classes. The test is carried out with four different scenarios and k values which are evaluated using a confusion matrix. Evaluation results showed the distribution of the dataset is 90:10 and the value of k = 1 is the best evaluation result, which is 88.11%. The feature selection was used for results by using Particle Swarm Optimization. The Particle Swarm Optimization used 20 iterations and 10 particles. It produced 97.9% the best evaluation accuracy, 96.17% precision, 96.62% recall, and 96.39% f-measure.