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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Media Sisfo Budimas : Jurnal Pengabdian Masyarakat STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Informatika dan Rekayasa Komputer Jurnal Pengabdian Masyarakat Unama
Silvia Rianti Agustini
Universitas Dinamika Bangsa
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document Jasmir Jasmir; Willy Riyadi; Silvia Rianti Agustini; Yulia Arvita; Despita Meisak; Lies Aryani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (340.414 KB) | DOI: 10.29207/resti.v6i4.4005

Abstract

In recent years, the application of deep learning methods has become increasingly popular, especially for big data, because big data has a very large data size and needs to be predicted accurately. One of the big data is the document text data of cancer clinical trials. Clinical trials are studies of human participation in helping people's safety and health. The aim of this paper is to classify cancer clinical texts from a public data set. The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Word Embedding Features (WE). This study has contributed to a new classification model for documenting clinical trials and increasing the classification performance evaluation. In this study, two experiments work are conducted, namely experimental work BiLSTM without WE, and experimental work BiLSTM using WE. The experimental results for BiLSTM without WE were accuracy = 86.2; precision = 85.5; recall = 87.3; and F-1 score = 86.4. meanwhile the experiment results for BiLSTM using WE stated that the evaluation score showed outstanding performance in text classification, especially in clinical trial texts with accuracy = 92,3; precision = 92.2; recall = 92.9; and F-1 score = 92.5.
Co-Authors Arfi Aiman Assyauqi Ari Andrianti Despita Meisak Despita Meisak Dr. Jasmir, S.Kom, M.Kom Febriyanti Gabriella Feranika, Ayu Hendri Hendri Inggrit Tia Pratiwi Inggrit Tia Septiana Jasmir Jasmir Jasmir Lies Aryani Mario Tamba Nurhadi Nurhadi Rohaini, Eni WILLY RIYADI Yulia Arvita