Optimalisasi algoritme voting feature intervals 5 menggunakan algoritme genetik pada data tuberkulosis paru
Abstract
We proposed a genetic algorithm to optimize VFI5 classification algorithm and to get the best
feature weights in lung tuberculocis data. This research used a uniform value for this features
weights which equals to one. The accuracy obtained was 83%. Genetic Algorithm (GA) is used to
optimize VFI5 by determining the optimal weights for each feature. GA will combine each weight
and search the best combination to get an optimal solution. In this research, GA can find the
optimal weight feature VFI5 classification algorithm in lung tuberculocis data. The optimal feature
weights are “the blood cough with average weight 0.91, limp with average weight 0.82, lost of
appetite with average weight 0.63, body weight decrease with average weight 0.62, fever and
perfiration with average weight 0.62”. in this reseached, 3-fold cross validation was used to divide
data into traning and testing and obtained 95% accuracy in each fold. In conclucions, this research has provided with and accurate model to predict TB and Non TB data instances.
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