ERINGKAS TEKS OTOMATIS PADA ARTIKEL BERBAHASA INDONESIA MENGGUNAKAN METODE MAXIMUM MARGINAL RELEVANCE

Peringkas teks otomatis atau automated text summarization adalah suatu metode untuk mengambil inti dari satu atau lebih dokumen teks. Peringkas Teks otomatis diperlukan untuk proses pembacaan, pencarian, dan pemahaman informasi menjadi lebih cepat dan efisien. Penelitian ini mengusulkan metode Maximum Marginal Relevance untuk mengerjakan proses peringkasan teks dengan otomatis. Metode dikembangkan dan diuji pada masing-masing 150 dokumen artikel berbahasa Indonesia. Ringkasan dihasilkan dari skor kemiripan antar kalimat yang dihitung menggunakan cosine similarity. Performa MMR dalam menghasilkan ringkasan dievaluasi menggunakan ROUGE(Recall-Oriented Understudy for Gisting Evaluation), digunakan untuk membandingkannya dengan ringkasan yang dibuat oleh manusia (gold standard). Hasil pengujian untuk tingkat kompresi 50%, memberikan nilai F1-score pada ROUGE-1, ROUGE-2, dan ROUGE-L masing-masing sebesar 71.86%, 64.18%, dan 71.56%. Sedangkan hasil pengujian dengan tingkat kompresi 30% menghasilkan F1-score untuk ROUGE-1, ROUGE-2, dan ROUGE-L masing-masing 62.95%, 53.61%, dan 62.47%. Dibandingkan penelitian terdahulu, penelitian ini menghasilkan skor yang lebih baik. Kata kunci: peringkas otomatis, cosine similarity, MMR, maximum marginal relevance, ROUG

Identifying Relevant Evidence for Systematic Reviews and Review Updates

Systematic reviews identify, assess and synthesise the evidence available to answer complex research questions. They are essential in healthcare, where the volume of evidence in scientific research publications is vast and cannot feasibly be identified or analysed by individual clinicians or decision makers. However, the process of creating a systematic review is time consuming and expensive. The pace of scientific publication in medicine and related fields also means that evidence bases are continually changing and review conclusions can quickly become out of date. Therefore, developing methods to support the creating and updating of reviews is essential to reduce the workload required and thereby ensure that reviews remain up to date. This research aims to support systematic reviews, thus improving healthcare through natural language processing and information retrieval techniques. More specifically, this thesis aims to support the process of identifying relevant evidence for systematic reviews and review updates to reduce the workload required from researchers. This research proposes methods to improve studies ranking for systematic reviews. In addition, this thesis describes a dataset of systematic review updates in the field of medicine created using 25 Cochrane reviews. Moreover, this thesis develops an algorithm to automatically refine the Boolean query to improve the identification of relevant studies for review updates. The research demonstrates that automating the process of identifying relevant evidence can reduce the workload of conducting and updating systematic reviews