Perbandingan Algoritma Dengan Particle Swarm Optimization Untuk Analisis Sentimen Pada Peraturan PSBB di Indonesia
DOI:
https://doi.org/10.31294/paradigma.v24i1.968Keywords:
Text Mining, Sentiment, AlgorithmAbstract
The pandemic has given rise to new rules and terms in society. Various countries have their own regulations, including Indonesia with the name PSBB for that, the author tries to conduct research related to the PSBB condition in Indonesia with the intent and purpose of knowing people's sentiments towards it, the authors carry out this modeling positively and negatively. model in a tweet on Twitter. We capture information through Twitter media which then we process the data so that it is ready to be tested on the algorithm used. In data collection and processing, we use a fast miner application. In this study, Naive Bayes,KNN,and SVM were used. We also did a model comparison with Particle Swarm Optimization. model 1 tested three algorithms using a 0.7-0.8 ratio validation and 10-fold cross-validation, In Model 2 the author used a selection feature, namely Particle swarm Optimization where PSO was used as optimization. From the second model, the accuracy is 88.00%. for SVM + PSO, 88.54%% for NB + PSO and 81.58% for K -NN + PSO. And after testing the 2 methods, it turns out that Naive Bayes + PSO has the highest level of accuracy and precision
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