Peringkasan Multi Dokumen Berbahasa Indonesia Menggunakan Whale Optimization Algorithm
Keywords:
swarm intelligence, whale optimization algorithm, multi documents summarizationAbstract
Document summarization is a natural language processing method that extracts information from one or more input text documents into an informative and accurate output. Swarm intelligence is one approach that can be used in making summaries that have superior performance. In this study, a multi-document summarization model for Indonesian language was created using one of the swarm intelligence methods, namely Whale Optimization Algorithm (WOA). In making the WOA model for this multi-document summary, 500 Indonesian news documents were used from various online news media. This dataset is divided into two categories, namely 80% for training data and 20% for testing data. In the training stage, WOA is used to optimize the weight of sentence features that are used as a reference in the process of selecting summary sentences. From the results of model testing and validation using k-fold cross validation, it was found that WOA get the best performance value compared to several other methods, namely Rouge-1 = 0.3946, Rouge-2 = 0.1859, Rouge-3 = 0.1309, and Rouge-L = 0.2226. The superiority of the k-fold cross validation results shows the consistency of the model's performance reliability in various document combinations.
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