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A COMPARISON OF COX PROPORTIONAL HAZARD AND RANDOM SURVIVAL FOREST MODELS IN PREDICTING CHURN OF THE TELECOMMUNICATION INDUSTRY CUSTOMER

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 4 (2022) · pp. 1433–1440

DOI
10.30598/barekengvol16iss4pp1433-1440
ISSN
2615-3017, 1978-7227
Published
2022-12-15
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The Cox Proportional hazard model is a popular method to analyze right-censored survival data. This method is efficient to use if the proportional hazard assumption is fulfilled. This method does not provide an accurate conclusion if these assumptions are not fulfilled. The new innovative method with a non-parametric approach is now developing to predict the time until an event occurs based on machine learning techniques that can solve the limitation of CPH. The method is Random Survival Forest, which analyzes right-censored survival data without regard to any assumptions. This paper aims to compare the predictive quality of the two methods using the C-index value in predicting right-censored survival data on churn data of the telecommunication industry customers with 2P packages consisting of Internet and TV, which are taken from all customer databases in the Jabodetabek area. The results show that the median value of the C-index of the RSF model is 0.769, greater than the median C-index value of the CPH model of 0.689. So the prediction quality of the RSF model is better than the CPH model in predicting the churn of the telecommunications industry customer.

Collaboration network of the authors

37 authors · 101 co-authorship links · 24 across different organisations

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

  • IPB University
  • Pakuan University
  • Badan Pusat Statistik
  • Universitas Negeri Surabaya
  • Bandung Islamic University
  • Binus University
  • Tadulako University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Aji Hamim WigenaIPB University2
INDAHWATI INDAHWATIIPB University2
Viarti EminitaAffiliation unknown2
Utami Dyah SyafitriIPB University2
I Made SumertajayaIPB University2
Bagus SartonoIPB University2
Anwar FitriantoIPB University2
Lukmanul HakimIPB University2
Enny KristianiIPB University1
Ujang SumarwanIPB University1
Lilik Noor YuliatiIPB University1
Wa Ode Rona FreyaAffiliation unknown1
Budi SusetyoAffiliation unknown1
Embay RohaetiPakuan University1
Wiwik Andriyani Lestari NingsihIPB University1
Budi SusetyoIPB University1
Satria June AdwendiBadan Pusat Statistik1
Khusnia Nurul KhikmahUniversitas Negeri Surabaya1
Khairil Anwar NotodiputroIPB University1
Rahmatin Nur AmaliaIPB University1
Leli LatifahIPB University1
Dwi Agustin Nuriani SirodjBandung Islamic University1
Anang KurniaIPB University1
Anang KurniaIPB University1
Asysta Amalia PasaribuBinus University1
Nurhidayah NurhidayahIPB University1
Anang KurniaIPB University1
Logananta Puja KusumaIPB University1
Agus Mohamad SolehIPB University1
Uswatun HasanahIPB University1
Rizka Amalia PutriRiau University1
Alimatun NajihaHasanuddin University1
Erlyne Nadhilah WidyaningrumMulawarman University1
Morina A. FathanTadulako University1

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 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A COMPARISON OF COX PROPORTIONAL HAZARD AND RANDOM SURVIVAL FOREST MODELS IN PREDICTING CHURN OF THE TELECOMMUNICATION INDUSTRY CUSTOMER</dc:title>
	<dc:creator>Nurhaliza, Sitti</dc:creator>
	<dc:creator>Sadik, Kusman</dc:creator>
	<dc:creator>Saefuddin, Asep</dc:creator>
	<dc:description xml:lang="en-US">The Cox Proportional hazard model is a popular method to analyze right-censored survival data. This method is efficient to use if the proportional hazard assumption is fulfilled. This method does not provide an accurate conclusion if these assumptions are not fulfilled. The new innovative method with a non-parametric approach is now developing to predict the time until an event occurs based on machine learning techniques that can solve the limitation of CPH. The method is Random Survival Forest, which analyzes right-censored survival data without regard to any assumptions. This paper aims to compare the predictive quality of the two methods using the C-index value in predicting right-censored survival data on churn data of the telecommunication industry customers with 2P packages consisting of&amp;nbsp; Internet and TV, which are taken from all customer databases in the Jabodetabek area. The results show that the median value of the C-index of the RSF model is 0.769, greater than the median C-index value of the CPH model of 0.689. So the prediction quality of the RSF model is better than the CPH model in predicting the churn of the telecommunications industry customer.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-12-15</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/6609</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss4pp1433-1440</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications; 1433-1440</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss4year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6609/5158</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Sitti Nurhaliza, Kusman Sadik, Asep Saefuddin</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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