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CONSTRUCTING AN OPTIMAL PORTFOLIO USING CLUSTERING LARGE APPLICATION AND VALUE AT RISK ANALYSIS FOR IDX80 STOCKS

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 20 No. 3 (2026) · pp. 1855–1868

DOI
10.30598/barekengvol20iss3pp1855-1868
ISSN
2615-3017, 1978-7227
Published
2026-04-08
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Investment is a way to manage wealth and achieve financial goals in the future. Stocks are an attractive investment instrument due to their high potential returns, although they also carry significant risks. These risks can be minimized through portfolio diversification. Diversification is carried out by selecting representative stocks from the clustering results. This study aims to construct an optimal portfolio using the Clustering Large Application (CLARA) method and conduct portfolio risk analysis using Value at Risk (VaR). The data used includes IDX80 stock closing prices from November 1, 2024, to January 31, 2025, the financial ratios of IDX80 stocks on December 2024, and the Bank Indonesia (BI-Rate) interest rate from November 2024 to January 2025. The CLARA method produces four stock clusters with a silhouette coefficient of 0.18226. This value indicates a low level of separation between clusters, as there might be overlapping features among the clusters. Representative stocks from each cluster are selected based on the highest Sharpe ratio: SCMA, JPFA, GOTO, and BRIS. The portfolio weights based on MVEP are 15.002% (SCMA), 29.786% (JPFA), 1.858% (GOTO), and 53.354% (BRIS). The VaR calculation shows a potential maximum loss of Rp137,139 in one day, with a 99% confidence level, from an initial investment of Rp10,000,000.

Collaboration network of the authors

36 authors · 78 co-authorship links

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Co-authorOrganisationJoint articles
Evy SulistianingsihAffiliation unknown9
Ray TamtamaAffiliation unknown8
Wirda AndaniAffiliation unknown4
Siti AprizkiyandariAffiliation unknown4
Dadan KusnandarAffiliation unknown2
Hani Dwi RetnaniAffiliation unknown2
Inggriani Millennia TaralyAffiliation unknown2
Steven Jansen SinagaAffiliation unknown2
Widad ImtiyazAffiliation unknown2
Dinda TiaraAffiliation unknown2
Fritzgerald Muhammad ArsyiAffiliation unknown2
Muhammad FauzanAffiliation unknown2
Nabila Izza HarnantaAffiliation unknown2
Nurhanifa NurhanifaAffiliation unknown2
Geby MarolaAffiliation unknown1
Rita ApriliyantiAffiliation unknown1
Tiffany ArdhithaAffiliation unknown1
Indah OktavianiAffiliation unknown1
Maria Welita DwinandaAffiliation unknown1
Lisa LestariAffiliation unknown1
Aditya Nugraha PratamaAffiliation unknown1
Naomi Nessyana DebatarajaAffiliation unknown1
Nurfitri Imro'ahAffiliation unknown1
Herina MarlisaAffiliation unknown1
Rifkah Alfiyyah SaryAffiliation unknown1
Ainun ZakiahAffiliation unknown1
Noerul HaninAffiliation unknown1
David Jordy DhandioAffiliation unknown1
Hairil Al-HamAffiliation unknown1
Preatin PreatinAffiliation unknown1
Louis Putra JayaAffiliation unknown1
Hendri KurniawanAffiliation unknown1
Yuyun Eka PratiwiAffiliation unknown1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">CONSTRUCTING AN OPTIMAL PORTFOLIO USING CLUSTERING LARGE APPLICATION AND VALUE AT RISK ANALYSIS FOR IDX80 STOCKS</dc:title>
	<dc:creator>Pujianti, Sania</dc:creator>
	<dc:creator>Perdana, Hendra</dc:creator>
	<dc:creator>Satyahadewi, Neva</dc:creator>
	<dc:description xml:lang="en-US">Investment is a way to manage wealth and achieve financial goals in the future. Stocks are an attractive investment instrument due to their high potential returns, although they also carry significant risks. These risks can be minimized through portfolio diversification. Diversification is carried out by selecting representative stocks from the clustering results. This study aims to construct an optimal portfolio using the Clustering Large Application (CLARA) method and conduct portfolio risk analysis using Value at Risk (VaR). The data used includes IDX80 stock closing prices from November 1, 2024, to January 31, 2025, the financial ratios of IDX80 stocks on December 2024, and the Bank Indonesia (BI-Rate) interest rate from November 2024 to January 2025. The CLARA method produces four stock clusters with a silhouette coefficient of 0.18226. This value indicates a low level of separation between clusters, as there might be overlapping features among the clusters. Representative stocks from each cluster are selected based on the highest Sharpe ratio: SCMA, JPFA, GOTO, and BRIS. The portfolio weights based on MVEP are 15.002% (SCMA), 29.786% (JPFA), 1.858% (GOTO), and 53.354% (BRIS). The VaR calculation shows a potential maximum loss of Rp137,139 in one day, with a 99% confidence level, from an initial investment of Rp10,000,000.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2026-04-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/20399</dc:identifier>
	<dc:identifier>10.30598/barekengvol20iss3pp1855-1868</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application; 1855-1868</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol20iss3year2026</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/20399/12673</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Sania Pujianti, Hendra Perdana, Neva Satyahadewi</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>