CLASSIFICATION ABSTRACT THESIS USING MACHINE LEARNING

Peneliti

Dr. Wartika, S.Kom, MT

Deskripsi/Abstrak

The purpose of this study is to find out which models have higher accuracy, precision, recall and f1-scores between the logistic regression, support vector machine, and decisiontree models. The data used in the form of 600 thesis abstract documents. the dataset was divided into 450 training data and 150 testing data. based on the analysis of logistic regression, support vector machine, and decisiontree applied to 150 testing data, the logistic regression model obtained an accuracy value of 79%, support vector machine obtained an accuracy value of 70%, and decisiontree obtained an accuracy value of 73%. The results of the analysis in the classification of the thesis abstract using the logistic regression, support vector machine, and decision tree models are good enough. Overall, it can be concluded that the logistic regression method has a higher accuracy value compared to support vector machine and decision tree. Further development can use other methods or algorithms to obtain higher accuracy values or it can also use smote techniques so that the results obtained can be aligned.