Peringkasan Teks Putusan Pengadilan Berbahasa Indonesia Menggunakan Algoritma TextRank
Keywords:
automatic text summarization, cosine similarity, court decisions, natural language processing, textrank, tf-idfAbstract
Court decisions are important legal documents that are often long and complex, which may cause difficulties for readers in understanding the meaning and may imply a lenghty reading time. This research raises the issue of how to develop a web-based text auto summarization application for court decision documents in Indonesian languange. This research uses mixed methods. As for the software development life cycle, we use the Agile methodology. Within the development we use a natural language processing approach that includes tokenization, text cleaning, stemming, term frequency-inverse document frequency (TF-IDF) calculation, and application of cosine similarity to measure the similarity between sentences before applying the TextRank algorithm. We use the Python programming language and the Flask web framework, utilizing libraries such as PyPDF2 for PDF processing, Sastrawi for the stemming process, and NetworkX for the implementation of the TextRank algorithm. The dataset used consists of 50 court decision documents. Evaluation of the application is carried out using precision, recall, and F-measure metrics, comparing the application's summarization results against the reference summary made by an expert. The test shows the highest precision value of 0.62 at a compression rate of 75%, demonstrating the application's ability to produce informative summaries. This research is expected to contribute to the development of text auto summarization applications on court decision documents, as well as to open up opportunities for further research.
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