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Forecasting Pertalite Stock Expenditures Using Exponential Smoothing and Linear Regression

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 4 (2024) · pp. 405–416

DOI
10.22146/ijccs.98849
ISSN
2460-7258, 1978-1520
Published
2024-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In the current industrial and business era, effective inventory management is essential for maintaining operational sustainability, particularly in the fuel industry. Pertalite, a popular fuel in Indonesia, with an octane number of 90, offers cleanliness, efficiency, and affordability. However, challenges arise in stock expenditure management due to inaccurate forecasting methods. Data mining, utilizing statistical and machine learning techniques, can identify patterns and trends for better stock forecasting. Recent studies highlight the effectiveness of exponential smoothing and linear regression in fuel demand forecasting. Exponential smoothing, which gives more weight to recent data, improves prediction accuracy, while linear regression analyzes the relationship between fuel stock and various independent variables. This study examines Pertalite fuel sales data from May 2022 to April 2024 from a Pertamina gas station in North Lampung. Results show that linear regression can predict trends, while exponential smoothing, using alpha values between 0.1 and 0.9, captures trends and variations over time. Both methods provide stable forecasts for specific months, demonstrating their utility in understanding Pertalite fuel sales patterns. The study underscores the importance of accurate forecasting in inventory management to meet market demands and maintain operational efficiency.

Keywords

Collaboration network of the authors

3 authors · 3 co-authorship links

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  • Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Noviana NovianaInstitut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia1
Deti NurdianahAffiliation 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.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Forecasting Pertalite Stock Expenditures Using Exponential Smoothing and Linear Regression</dc:title>
	<dc:creator>afandi, asep afandi</dc:creator>
	<dc:subject xml:lang="en-US">Information Systems Study Program, Faculty Of Computer Science, Institute Of Business Technology And Language Dian Cipta Cendikia, Lampung, Indonesia</dc:subject>
	<dc:subject xml:lang="en-US">Exponential Smoothing, Linear Regression, Pertalite, Forecast</dc:subject>
	<dc:description xml:lang="en-US">In the current industrial and business era, effective inventory management is essential for maintaining operational sustainability, particularly in the fuel industry. Pertalite, a popular fuel in Indonesia, with an octane number of 90, offers cleanliness, efficiency, and affordability. However, challenges arise in stock expenditure management due to inaccurate forecasting methods. Data mining, utilizing statistical and machine learning techniques, can identify patterns and trends for better stock forecasting. Recent studies highlight the effectiveness of exponential smoothing and linear regression in fuel demand forecasting. Exponential smoothing, which gives more weight to recent data, improves prediction accuracy, while linear regression analyzes the relationship between fuel stock and various independent variables. This study examines Pertalite fuel sales data from May 2022 to April 2024 from a Pertamina gas station in North Lampung. Results show that linear regression can predict trends, while exponential smoothing, using alpha values between 0.1 and 0.9, captures trends and variations over time. Both methods provide stable forecasts for specific months, demonstrating their utility in understanding Pertalite fuel sales patterns. The study underscores the importance of accurate forecasting in inventory management to meet market demands and maintain operational efficiency.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia</dc:contributor>
	<dc:date>2024-10-31</dc:date>
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	<dc:identifier>10.22146/ijccs.98849</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 4 (2024): October; 405-416</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>info:eu-repo/grantAgreement/EC/FP7/123456</dc:relation>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/98849/40368</dc:relation>
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	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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