Rama Jayapermana's scientific contributions

Publication (1)

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Purpose: However, from the variety of uses of these algorithms, in general, accuracy problems are still a concern today, even accuracy problems related to multi-class classification still require further research.Methods: This study proposes a stacking ensemble classifier method to produce better accuracy by combining Logistic Regression, Random Fo...


... Another method are Support Vector Machine (SVM) [14][15] [16], and Backpropagation Neural Network [17]. Naive Bayes has the advantage of being simple but has high accuracy even though it uses not a lot of data, while SVM has the advantage of solving linear and non-linear classification and regression problems which can become a learning algorithm capability for regression and classification, but the Support Vector Machine (SVM) are not efficient in training large-capacity data [18]. Backpropagation has advantages over other Neural Networks, namely Backpropagation using supervised training [19]. ...