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Comparison of the Effectiveness of the Weighted Moving Average Method and Single Exponential Smoothing for Forecasting the Number of Telkomsel Customers

SemanTIK: Teknik Informasi · Vol. 5 No. 2 (2019) · pp. 255–262

DOI
10.55679/semantik.v5i2.7210
ISSN
2502-8928, 2460-1446
Published
2019-12-18
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

PT. Telkomsel almost controls 90% market share in Indonesia with more than 196 million subscribers in 2017. Grapari Kendari City as a service center for Telkomsel customers in Southeast Sulawesi still does not have a system to predict the number of its customers. This resulted in the loss of potential services that should have been able to be provided such as the construction of branch offices in areas that have the potential to have a high number of customers to provide overall service to customers. To overcome this problem, a system will be created that can assist in managing customer data and predict the number of customers using forecasting algorithms with weighted moving average methods and single exponential smoothing methods as a comparison to see the effectiveness of the methods used. After a comparative analysis was found, 6 of the 8 cities / regencies used as the research data showed the Mean Absolute Error (MAD), the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) Single Exponential Smoothing method smaller than the Weighted Moving Average method. This is because the Single Exponential Smoothing method is in accordance with the data pattern of growth in the number of Telkomsel Region Southeast Sulawesi customers, namely horizontal data patterns or data patterns that fluctuate around a constant average value.

Keywords

Collaboration network of the authors

40 authors · 120 co-authorship links · showing the 40 most connected

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

  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sutardi SutardiUniversitas Halu Oleo8
Ika Purwanti NingrumUniversitas Halu Oleo7
Adha Mashur SajiahUniversitas Halu Oleo5
Isnawaty IsnawatyUniversitas Halu Oleo5
Natalis RansiUniversitas Halu Oleo5
Jumadil NangiUniversitas Halu Oleo4
Isnawaty IsnawatyAffiliation unknown4
LM TajidunUniversitas Halu Oleo4
Rahmat RamadhanUniversitas Halu Oleo3
Subardin SubardinAffiliation unknown3
Muhammad Ishak JaelaniUniversitas Halu Oleo2
Try Sugiyarto SoeparyantoUniversitas Halu Oleo2
Muh YaminUniversitas Halu Oleo2
Hastuti HastutiAffiliation unknown2
Rizal Adi SaputraUniversitas Halu Oleo2
Nurul Hajra Hadijah HajraUniversitas Halu Oleo1
Aditya PutraUniversitas Halu Oleo1
StatiswatyUniversitas Halu Oleo1
Lirna Frischa Dwi PutriAffiliation unknown1
Raviq Gandhu LahadiAffiliation unknown1
Jayanti Yusmah SariUniversitas Halu Oleo1
Linda Purnama MuriUniversitas Halu Oleo1
Niarma NiarmaUniversitas Halu Oleo1
Wa Ode ErmaliantiUniversitas Halu Oleo1
Gusti Made Cahya WiarjayaAffiliation unknown1
Azkar KumalaAffiliation unknown1
La Ode ApriadiAffiliation unknown1
LM. Fid AksaraUniversitas Halu Oleo1
Tamsil TajsamAffiliation unknown1
Ardiyanti PutriAffiliation unknown1
Ramadhin ZaidUniversitas Halu Oleo1
Nur Imran RuslanAffiliation unknown1
Masna JumiyatiAffiliation unknown1
La Ode HasnuddinAffiliation unknown1
Holis HermansyahUniversitas Halu Oleo1
Apriadi BaharAffiliation unknown1
Laode Hasnuddin S SagalaAffiliation 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://ojs.uho.ac.id/index.php/semantik/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Comparison of the Effectiveness of the Weighted Moving Average Method and Single Exponential Smoothing for Forecasting the Number of Telkomsel Customers</dc:title>
	<dc:creator>Sinatra, Idzanul Iksan</dc:creator>
	<dc:creator>Pramono, Bambang</dc:creator>
	<dc:creator>Statiswaty, Statiswaty</dc:creator>
	<dc:subject xml:lang="en-US">Software Engineering</dc:subject>
	<dc:description xml:lang="en-US">PT. Telkomsel almost controls 90% market share in Indonesia with more than 196 million subscribers in 2017. Grapari Kendari City as a service center for Telkomsel customers in Southeast Sulawesi still does not have a system to predict the number of its customers. This resulted in the loss of potential services that should have been able to be provided such as the construction of branch offices in areas that have the potential to have a high number of customers to provide overall service to customers. To overcome this problem, a system will be created that can assist in managing customer data and predict the number of customers using forecasting algorithms with weighted moving average methods and single exponential smoothing methods as a comparison to see the effectiveness of the methods used. After a comparative analysis was found, 6 of the 8 cities / regencies used as the research data showed the Mean Absolute Error (MAD), the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) Single Exponential Smoothing method smaller than the Weighted Moving Average method. This is because the Single Exponential Smoothing method is in accordance with the data pattern of growth in the number of Telkomsel Region Southeast Sulawesi customers, namely horizontal data patterns or data patterns that fluctuate around a constant average value.</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-12-18</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs.uho.ac.id/index.php/semantik/article/view/7210</dc:identifier>
	<dc:identifier>10.55679/semantik.v5i2.7210</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol 5, No 2 (2019): semanTIK; 255-262</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:source>10.55679/semantik.v5i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/semantik/article/view/7210/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 semanTIK</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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