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Berdasarkan pemaparan dari penelitian sebelumnya, maka peneliti akan melakukan komparasi algoritma Support Vector Machine (SVM) dan Naive Bayes (NB) pada
This study aims to examine four machine learning algorithms, Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), in geometry-
Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for
Among the classification techniques J48, Random Forest, Random Tree, Decision Table, MLP, Naïve Bayes, and Bayes Network, the Random Forest classifier has achieved the highest accuracy
Gradient Boosting, Decision Tree, Random Forest, SVM, KNN, and Logistic Regression are some of the Supervised ML classifiers employed in this study to deploy a model for heart
Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree have been used for the recognition of digits using Waikato Environment for
This paper aims to compare traditional machine learning models Naïve Bayes NB and Support Vector Machine SVM with deep learning models Long Short-Term Memory LSTM, Neural Network with
This language identification process employed six classification models: Support Vector Machine SVM, Naïve Bayes Classifier NBC, Decision Tree DT, Rocchio Classification RC, Logistic
