Forecasting Pertalite Stock Expenditures Using Exponential Smoothing and Linear Regression
asep afandi afandi(1*)
(1) ITBA Dian Cipta Cendikia
(*) Corresponding Author
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.
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Z. Malara And P. Ziembicki, “Information gap in value propositions of business models of language schools,” Oper. Res. Decis., vol. 30, no. 2, pp. 59–76, 2020, doi: 10.37190/ORD200202.
E. B. Setyawan, A. Yunita, and S. R. Sekarjatiningrum, “International Journal On Informatics Visualization journal homepage : www.joiv.org/index.php/joiv International Journal On Informatics Visualization Development Of Automatic Real Time Inventory Monitoring System using RFID Technology in Warehouse,” vol. 6, no. September, pp. 636–642, 2022, [Online]. Available: www.joiv.org/index.php/joiv
Z. Muhamad, S. Sulistyo, N. Sri, and B. Adry, “Comparison of Three Way Catalytic Converter Exhaust Gas with Pertalite, Pertamax, and Pertamax Turbo Fuel in Gasoline Motor,” Am. Sci. Res. J. Eng. Technol. Sci., vol. 57, no. 1, pp. 97–108, 2019.
F. N. Iskandar, R. N. A. Wijaya, and S. W. Goeritno, “Performance Evaluation of Different Octane Numbers on Toyota Yaris Through Dynamometer Test and Engine Simulation”.
F. Liantoni and A. Agusti, “Forecasting bitcoin using double exponential smoothing method based on mean absolute percentage error,” Int. J. Informatics Vis., vol. 4, no. 2, pp. 91–95, 2020, doi: 10.30630/joiv.4.2.335.
R. Kannan, C. C. Jet, K. Ramakrishnan, and S. Ramdass, “Predicting Student’s Soft Skills Based on Socio-Economical Factors: An Educational Data Mining Approach,” Int. J. Informatics Vis., vol. 7, no. 3–2, pp. 2040–2047, 2023, doi: 10.30630/joiv.7.3-2.2342.
G. Airlangga, A. Rachmat, and D. Lapihu, “Comparison of exponential smoothing and neural network method to forecast rice production in Indonesia,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 3, pp. 1367–1375, 2019, doi: 10.12928/TELKOMNIKA.V17I3.11768.
L. Sucipto and S. Syaharuddin, “Konstruksi forecasting system multi-model untuk pemodelan matematika pada peramalan indeks pembangunan manusia provinsi nusa tenggara barat,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 4, no. 2, pp. 114–124, 2018, doi: 10.26594/register.v4i2.1263.
S. Janani and A. S. Santhi, “Multiple linear regression model for mechanical properties and impact resistance of concrete with fly ash and hooked-end steel fibers,” Int. J. Technol., vol. 9, no. 3, pp. 526–536, 2018, doi: 10.14716/ijtech.v9i3.763.
I. Journal, “Estimation of Equivalency Units for Vehicle Types Under,” pp. 820–829, 2017.
E. H. Nugrahani, S. Nurdiati, F. Bukhari, M. K. Najib, D. M. Sebastian, and P. A. N. Fallahi, “Sensitivity and feature importance of climate factors for predicting fire hotspots using machine learning methods,” IAES Int. J. Artif. Intell., vol. 13, no. 2, pp. 2210–2223, 2024, doi: 10.11591/ijai.v13.i2.pp2212-2225.
A. Essayad and K. M. Abdellah, “Predicting baccalaureate student result to prevent failure: a hybrid model approach,” IAES Int. J. Artif. Intell., vol. 13, no. 1, pp. 764–774, 2024, doi: 10.11591/ijai.v13.i1.pp764-774.
DOI: https://doi.org/10.22146/ijccs.98849
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