Classification of Book Types Using the Support Vector Machine (SVM) Method
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Abstract
This study aims to create a model that can classify book types based on several categories and analyze the accuracy results of the Support Vector Machine (SVM) method. This research begins with the stages of data collection, namely the dataset of books obtained from the library. Furthermore, the dataset will be categorized into several types. The next stage, after the data is collected, will be carried out in the pre-process stage. This pre-process stage aims to prepare data so that it is ready to be processed in the feature extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stopword removal, and stemming. Next, the feature extraction stage will be carried out which aims to explore potential information and represent words as feature vectors. The next stage is to separate the training data and test data. Then the classification process is carried out using the SVM multiclass method to get the final result of modeling. The resulting classification results will then be evaluated in order to obtain an accuracy value and then will be analyzed whether the resulting classification model is feasible to implement.
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