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Open access Jurnal SINTA 2

LOOK ALIKE-SOUND ALIKE PREDICTION AS A TOOL FOR PATIENT SAFETY

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 1 (2025) · pp. 85–94

DOI
10.22146/ijccs.103210
ISSN
2460-7258, 1978-1520
Published
2025-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Report from the WHO that one of the highest causes of medication errors is Look Alike – Sound Alike (LASA) drugs, leading to errors in receiving information about the drugs, which of course will affect patient safety. Efforts to reduce medication errors have been widely implemented, such as conducting medication training, managing medications, and storing and labeling medications. However, all of that leads to human error, so the utilization of technology is needed to address this issue. The technology expected to help reduce medication errors is the utilization of artificial intelligence (AI). AI is designed for automation processes and systems that can learn independently, allowing the causes of medication errors such as LASA to be learned by the system and predicted automatically. Deep learning is a part of AI that works by providing solutions accurately and automatically. The Recurrent Neural Networks (RNN) algorithm is one of the deep learning methods that has been proven accurate in predictions based on previous research studies. In this study, LASA predictions were made using RNN with the aim of serving as an aid to reduce medication errors, thereby ensuring patient safety. The accuracy achieved is 99% for training and 81% for testing.

Keywords

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2 authors · 1 co-authorship link

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  • Universitas Gadjah Mada
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Agfianto Eko PutraUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">LOOK ALIKE-SOUND ALIKE PREDICTION AS A TOOL FOR PATIENT SAFETY</dc:title>
	<dc:creator>anggiratih, endang</dc:creator>
	<dc:subject xml:lang="en-US">Informatics</dc:subject>
	<dc:subject xml:lang="en-US">Prediction; Medication Error; Look Alike – Sound Alike; Deep learning; Recurrent Neural Networks</dc:subject>
	<dc:description xml:lang="en-US">Report from the WHO that one of the highest causes of medication errors is Look Alike – Sound Alike (LASA) drugs, leading to errors in receiving information about the drugs, which of course will affect patient safety. Efforts to reduce medication errors have been widely implemented, such as conducting medication training, managing medications, and storing and labeling medications. However, all of that leads to human error, so the utilization of technology is needed to address this issue. The technology expected to help reduce medication errors is the utilization of artificial intelligence (AI). AI is designed for automation processes and systems that can learn independently, allowing the causes of medication errors such as LASA to be learned by the system and predicted automatically. Deep learning is a part of AI that works by providing solutions accurately and automatically. The Recurrent Neural Networks (RNN) algorithm is one of the deep learning methods that has been proven accurate in predictions based on previous research studies. In this study, LASA predictions were made using RNN with the aim of serving as an aid to reduce medication errors, thereby ensuring patient safety. The accuracy achieved is 99% for training and 81% for testing.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Kemendikbudristek</dc:contributor>
	<dc:date>2025-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/103210</dc:identifier>
	<dc:identifier>10.22146/ijccs.103210</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 1 (2025): January; 85-94</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/103210/40998</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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