Melola Discovery

← Melola Discovery

Open access Jurnal

Breast Cancer Classification Model Using Decision Tree Algorithm

Jurnal Matematika, Statistika dan Komputasi · Vol. 22 No. 3 (2026) · pp. 617–626

DOI
10.20956/j.v22i3.49472
ISSN
2614-8811, 1858-1382
Published
2026-05-14
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Cancer is a disease characterized by the presence of abnormal cells or tissues that grow rapidly, uncontrollably, can spread to other parts of the patient's body and it can also sometimes be malignant. According to the International Agency for Research on Cancer, in 2024 breast cancer will rank second in terms of the highest number of cases and fourth as the leading cause of death globally. The objective of this study is to apply the Classification and Regression Tree (CART) decission tree algorithm to a breast cancer classification model based on patient medical records. The model developed has a specificity of 95.77%, recall, precision, and F1-Score of 93.02%, and accuracy of 94.74%. The model was evaluated using a confusion matrix to measure its performance. Thus, the CART algorithm can be applied in classification models, and the resulting model is considered optimal as it achieves percentages within the 90%-100% range for all performance evaluation metrics.

Keywords

Collaboration network of the authors

23 authors · 37 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Medan
  • Satya Wacana Christian University
  • STMIK Bumigora Mataram
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad Rijal AlfianAffiliation unknown2
La Onu La OlaAffiliation unknown1
Utama K. PangerangAffiliation unknown1
Sitti Wahidah MasnaniAffiliation unknown1
Tirsa AminahAffiliation unknown1
Fauziah Khairani LubisState University of Medan1
Adi SetiawanSatya Wacana Christian University1
Khairul AnsharAffiliation unknown1
Zainuddin GintingAffiliation unknown1
Abdurahim AbdurahimSTMIK Bumigora Mataram1
Jann-Long ChernAffiliation unknown1
Nirwanto NirwantoAffiliation unknown1
Lisa HarsyiahAffiliation unknown1
Lailia AwalushaumiAffiliation unknown1
Bulqis Nebulla SyechahAffiliation unknown1
Satriawan PradanaAffiliation unknown1
septia amrylianaAffiliation unknown1
Rohmatul UlyaAffiliation unknown1
Andi Lukman IrwanAffiliation unknown1
Nurlinah NurlinahAffiliation 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://journal.unhas.ac.id/index.php/jmsk/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">Breast Cancer Classification Model Using Decision Tree Algorithm</dc:title>
	<dc:creator xml:lang="en">Robbaniyyah, Nuzla Af'idatur</dc:creator>
	<dc:creator xml:lang="en">Asmawati, Ismi</dc:creator>
	<dc:creator xml:lang="en">Bahri, Syamsul</dc:creator>
	<dc:subject xml:lang="en">Breast Cancer</dc:subject>
	<dc:subject xml:lang="en">,Decission Tree Algorithm</dc:subject>
	<dc:subject xml:lang="en"> CART</dc:subject>
	<dc:description xml:lang="en">Cancer is a disease characterized by the presence of abnormal cells or tissues that grow rapidly, uncontrollably, can spread to other parts of the patient's body and it can also sometimes be malignant. According to the International Agency for Research on Cancer, in 2024 breast cancer will rank second in terms of the highest number of cases and fourth as the leading cause of death globally. The objective of this study is to apply the Classification and Regression Tree (CART) decission tree algorithm to a breast cancer classification model based on patient medical records. The model developed has a specificity of 95.77%, recall, precision, and F1-Score of 93.02%, and accuracy of 94.74%. The model was evaluated using a confusion matrix to measure its performance. Thus, the CART algorithm can be applied in classification models, and the resulting model is considered optimal as it achieves percentages within the 90%-100% range for all performance evaluation metrics.
 </dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2026-05-14</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://journal.unhas.ac.id/index.php/jmsk/article/view/49472</dc:identifier>
	<dc:identifier>10.20956/j.v22i3.49472</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 22 No. 3 (2026): May 2026; 617-626</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/49472/14414</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>