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Big Data Analytics Algorithms for Dynamic Pricing: The Legal Analysis of the Indonesia Competitions Law readiness in Digital Era

Jurnal IUS (Kajian Hukum dan Keadilan) · Vol. 12 No. 1 (2024) · hlm. 68–90

DOI
10.29303/ius.v12i1.1303
ISSN
2477-815X, 2303-3827
Terbit
2024-04-29
Penerbit
Magister of Law, Faculty of Law, University of Mataram
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This article analyzes the readiness of Indonesian competition law regarding the utilization of big data and reinforcement learning as tools to improve retailers' pricing strategies, ultimately leading to increased profitability and higher customer engagement and loyalty. It conducts a comprehensive review of scholarly literature pertaining to adaptive algorithmic pricing, with a specific focus on analyzing trends and the impacts of algorithmic pricing strategies. The literature review spans the years 2018 to 2022 and adheres to PRISMA criteria, with academic journals from Scopus serving as the primary source of research papers. The findings of this review indicate that it is evident that the most frequently utilized type of algorithm is RL, that shares a resemblance to human learning processes. Competition law enforcement should consider the possibility of illicit agreements between these artificial agents of colluding companies. In light of the capacity of EAs to facilitate the coordination of illicit agreement, it is imperative to consider the reformulation of Article 5 of Law Number 5 of 1999 and Regulation of KPPU Number 4 of 2011, particularly regarding the classification of price-fixing, to be adjusted to the latest developments, particularly regarding "EAs". ITE Law also should not be limited to EAs merely acting as “tools”. Instead, it should acknowledge their capacity to function as “AI Agents” capable of autonomous action.

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Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://jurnalius.ac.id/ojs/index.php/jurnalIUS/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Big Data Analytics Algorithms for Dynamic Pricing: The Legal Analysis of the Indonesia Competitions Law readiness in Digital Era</dc:title>
	<dc:creator>Tejomurti, Kukuh</dc:creator>
	<dc:creator>Sukarmi, Sukarmi</dc:creator>
	<dc:creator>Santoso, Budi</dc:creator>
	<dc:creator>Widhiyanti, Hanif Nur</dc:creator>
	<dc:subject xml:lang="en-US">Algorithmic Pricing</dc:subject>
	<dc:subject xml:lang="en-US">Dynamic Pricing</dc:subject>
	<dc:subject xml:lang="en-US">Legal Analysis</dc:subject>
	<dc:description xml:lang="en-US">This article analyzes the readiness of Indonesian competition law regarding the utilization of big data and reinforcement learning as tools to improve retailers' pricing strategies, ultimately leading to increased profitability and higher customer engagement and loyalty. It conducts a comprehensive review of scholarly literature pertaining to adaptive algorithmic pricing, with a specific focus on analyzing trends and the impacts of algorithmic pricing strategies. The literature review spans the years 2018 to 2022 and adheres to PRISMA criteria, with academic journals from Scopus serving as the primary source of research papers. The findings of this review indicate that it is evident that the most frequently utilized type of algorithm is RL, that shares a resemblance to human learning processes. Competition law enforcement should consider the possibility of illicit agreements between these artificial agents of colluding companies. In light of the capacity of EAs to facilitate the coordination of illicit agreement, it is imperative to consider the reformulation of Article 5 of Law Number 5 of 1999 and Regulation of KPPU Number 4 of 2011, particularly regarding the classification of price-fixing, to be adjusted to the latest developments, particularly regarding "EAs". ITE Law also should not be limited to EAs merely acting as “tools”. Instead, it should acknowledge their capacity to function as “AI Agents” capable of autonomous action.</dc:description>
	<dc:publisher xml:lang="en-US">Magister of Law, Faculty of Law, University of Mataram</dc:publisher>
	<dc:date>2024-04-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-Reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jurnalius.ac.id/ojs/index.php/jurnalIUS/article/view/1303</dc:identifier>
	<dc:identifier>10.29303/ius.v12i1.1303</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal IUS Kajian Hukum dan Keadilan; Vol. 12 No. 1: April 2024: Jurnal IUS Kajian Hukum dan Keadilan; 68-90</dc:source>
	<dc:source>2477-815X</dc:source>
	<dc:source>2303-3827</dc:source>
	<dc:source>10.29303/ius.v12i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jurnalius.ac.id/ojs/index.php/jurnalIUS/article/view/1303/607</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Kukuh Tejomurti, Sukarmi Sukarmi, Budi Santoso, Hanif Nur Widhiyanti</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>