Optimasi Parameter Support Vector Machine Dengan Algoritma Genetika Untuk Analisis Sentimen Pada Media Sosial Instagram

Social media is an online media that users use to interact with each other by expressing themselves by giving comments, and one example is Instagram. All the collected comments will form a public opinion. This opinion can be used with sentiment analysis to become information. The method commonly used to carry out sentiment analysis is classification using machine learning. One of the machine learning that is often used is the Support Vector Machine (SVM). However, on non-linear problems such as sentiment analysis, SVM requires the kernel to map vectors into high-dimensional spaces to solve non-linear problems. The problem faced in using the kernel is to choose the optimal parameters for the classification model to produce a good classification model. This study proposes a new approach to obtain optimal parameters for SVM using Genetic Algorithm (GA). This study designed an SVM-GA classification model from the data collection, processing, classification, and evaluation stages. The results showed that the best accuracy produced with parameters optimized with the genetic algorithm was 81.6%, or an increase of 2.4% from the SVM sentiment analysis method without GA optimization.