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Modeling OTP Delivery Notification Status through a Causality Bayesian Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 1 (2024) · pp. 61–72

DOI
10.22146/ijccs.90030
ISSN
2460-7258, 1978-1520
Published
2024-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Digital money is the fundamental driving factor behind today's modern economy. Credit/debit cards, e-wallets, and other contactless payment options are widely available nowadays. This also raises the security risk associated with passwords in online transactions. One-time passwords (OTPs) are another option for mitigating this. A one-time password (OTP) serves as an additional password authentication or validation technique for each user authentication session. Failures in transmitting OTP passwords through SMS can arise owing to operator network faults or technological concerns.To minimize the risk value that arises in online transactions, it is necessary to evaluate the causality of the OTP SMS sending transaction status category by determining the main factors for successful OTP SMS sending and identifying the causes of failure when sending OTP SMS using the Bayesian Network method. According to data analysis, online transactions occur more frequently in the morning, with status summaries such as no delay, unknown status, and others. Furthermore, there is causality with at least three variables in the principal status summary, including no delay, uncertain summary, long delay, normal, likely operator issues, abnormal, and more. With a high accuracy rate of around 90% in forecasting the likelihood of recurrence.

Keywords

Collaboration network of the authors

5 authors · 6 co-authorship links · 2 across different organisations

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  • Islamic University of Indonesia
  • Universitas Gadjah Mada
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Amanda Lailatul FadhilahIslamic University of Indonesia1
Afiahayati AfiahayatiUniversitas 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">Modeling OTP Delivery Notification Status through a Causality Bayesian Network</dc:title>
	<dc:creator>Asriny, Novendri Isra</dc:creator>
	<dc:creator>Dewa, Chandra Kusuma</dc:creator>
	<dc:creator>Luthfi, Ahmat</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">online transaction;one-time password; SMS transaction;machine learning;bayesian network</dc:subject>
	<dc:description xml:lang="en-US">Digital money is the fundamental driving factor behind today's modern economy. Credit/debit cards, e-wallets, and other contactless payment options are widely available nowadays. This also raises the security risk associated with passwords in online transactions. One-time passwords (OTPs) are another option for mitigating this. A one-time password (OTP) serves as an additional password authentication or validation technique for each user authentication session. Failures in transmitting OTP passwords through SMS can arise owing to operator network faults or technological concerns.To minimize the risk value that arises in online transactions, it is necessary to evaluate the causality of the OTP SMS sending transaction status category by determining the main factors for successful OTP SMS sending and identifying the causes of failure when sending OTP SMS using the Bayesian Network method. According to data analysis, online transactions occur more frequently in the morning, with status summaries such as no delay, unknown status, and others. Furthermore, there is causality with at least three variables in the principal status summary, including no delay, uncertain summary, long delay, normal, likely operator issues, abnormal, and more. With a high accuracy rate of around 90% in forecasting the likelihood of recurrence.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-01-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/90030</dc:identifier>
	<dc:identifier>10.22146/ijccs.90030</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 1 (2024): January; 61-72</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/90030/38504</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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