Metode Double Exponential Smoothing Dan Arima Untuk Meramalkan Kebutuhan Air Pelanggan PT Petro Karya Niaga

Mualief, Muhammad (2023) Metode Double Exponential Smoothing Dan Arima Untuk Meramalkan Kebutuhan Air Pelanggan PT Petro Karya Niaga. Jurnal Peradaban Sains, Rekayasa dan Teknologi, 11 (2). pp. 392-406. ISSN 2686-553X

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Abstract

This research aims to obtain the best forecasting method in predicting the water needs of PT PKN customers by comparing the double exponential smoothing method and the ARIMA method. The data used is PT Petro Karya Niaga's water demand for the period June 2020 to September 2023. Based on this research. PT PKN Sales demand prediction results using the Double Exponential Smoothing method from the period October 2023 - March 2024 respectively in October 2023 amounted to 833,596, November 2023 amounted to 825,279, December 2023 amounted to 816,961, January 2024 amounted to 808,643, February 2024 amounted to 800,326, March 2024 amounting to 792,008. The error value obtained is MSE 44405.82. Based on the results of identifying the ARIMA model, there is only 1 model that is suitable to be used as a forecasting equation model, namely ARIMA (0,2,1). The ARIMA (0,2,1) model has an MSE value of 50168. So it can be concluded that the model is the best used is the ARIMA model (0,2,1) for PT Petro Karya Niaga's water demand forecasting model. The prediction results obtained from 2023 - March 2024 respectively are 810.57; 796.78, 783.65, 771.17, 759.33 and 748.15. The double exponential smoothing method is superior to the ARIMA method in predicting water needs at PT PKN because it produces a smaller MSE value and faster computing time than ARIMA.

Item Type:	Article
Uncontrolled Keywords:	ARIMA, Double Exponential Smoothing, MSE
Subjects:	Engineering > Industrial Engineering Engineering
Divisions:	Faculty of Engineering > Industrial Engineering Study Program
Depositing User:	Muhammad Mualief
Date Deposited:	24 Feb 2024 17:34
Last Modified:	24 Feb 2024 17:34
URI:	http://eprints.umg.ac.id/id/eprint/11152

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