Naïve Bayes Classifier dengan Particle Optimize Weight Forward pada Dataset
DOI:
https://doi.org/10.47065/bulletincsr.v3i6.290Keywords:
Naïve Bayes Classifier; Particle Optimize Weights Forward; Classification; Data MiningAbstract
Classification is the process of identifying and grouping objects into classes or categories based on their characteristics. In data mining, there are two processes, namely classification and clustering, which are used to group objects based on similarities. In the classification process, various methods such as K-NN, SVM, and Naïve Bayes are often used and developments are made in the method. The Naïve Bayes classifier is proven to have advantages, such as faster calculation and better accuracy. However, this method has limitations in the attribute selection process. To overcome this limitation, the Particle Optimize Weights Forward algorithm is used to improve accuracy by assigning weights to attributes in the Naïve Bayes method. This approach improves the efficiency and effectiveness of the Naïve Bayes classifier in data classification tasks.
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