PERBANDINGAN ANALISIS PERAMALAN ARIMA, SARIMA DAN EXPONENTIAL SMOOTHING HOLT-WINTERS TERHADAP HARGA BATUBARA

ALZA, ARIA PUTRI and Yuli Andriani, Yuli and Endang Sri Kresnawati, Endang (2024) PERBANDINGAN ANALISIS PERAMALAN ARIMA, SARIMA DAN EXPONENTIAL SMOOTHING HOLT-WINTERS TERHADAP HARGA BATUBARA. Undergraduate thesis, Sriwijaya University.

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Abstract

This study compares the accuracy of ARIMA, SARIMA, and Exponential Smoothing Holt-Winters models in forecasting coal prices. Coal prices that change over time require price forecasting to support decision making. Therefore, this study forecasts coal prices through ARIMA, SARIMA and Exponential Smoothing Holt-Winters to obtain the best method in forecasting coal prices from November 2024 to October 2025. The data used are monthly coal price data from September 2017-August 2023 for training data and September 2023 - October 2024 for testing data. The best model is selected based on accuracy criteria such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results of the study indicate that Exponential Smoothing Holt-Winters (multiplicative) produces higher accuracy than ARIMA and SARIMA by obtaining an MSE of 230.2615, an MAE value of 12.40 and a MAPE value of 9.7963% which indicates a very good forecasting model, so this model has a higher level of accuracy for predicting coal prices. The suggestion in this study is that further researchers can use data with different conditions to compare the level of forecast accuracy produced.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	ARIMA, SARIMA, Exponential Smoothing Holt-Winters, peramalan harga, batubara.
Subjects:	Q Science > QA Mathematics > QA1-43 General Q Science > QA Mathematics > QA1-939 Mathematics > QA1.T553 Mathematics--Periodicals. Computer science--Periodicals. Computer science.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Aria Putri Alza
Date Deposited:	26 Nov 2024 01:48
Last Modified:	26 Nov 2024 01:48
URI:	http://repository.unsri.ac.id/id/eprint/159795

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