BibTex Citation Data :
@article{J.Gauss39095, author = {Iva Amalia and Tatik widiharih and Tarno Tarno}, title = {HOLT WINTERS EXPONENTIAL SMOOTHING UNTUK MERAMALKAN PRODUK DOMESTIK BRUTO DI INDONESIA}, journal = {Jurnal Gaussian}, volume = {13}, number = {1}, year = {2024}, keywords = {GDP, Holt Winters, MAPE}, abstract = { A country's economic growth will be seen as having grown better or worse than in the past by measuring based on the increase in Gross Domestic Product (GDP). The pattern of Indonesian GDP from 2010 to 2022 shows that the data increases from year to year and there are seasonal fluctuations in the quarter. Holt Winters method is part of the Exponential Smoothing method used for forecasting if the data shows a trend and seasonality in the data pattern. The Holt Winters method has two models, namely additive and multiplicative. Holt Winters Additive is used if the data shows trends and seasonal patterns remain constant. Multiplicative Holt Winter is used if the data shows trends and seasonal patterns proportional to the average rate of the seasonal time series. The data used in this study are GDP Based on Current Prices (Nominal GDP) and GDP on the Basis of Constant Prices (Real GDP). Based on the evaluation of model performance using test data forecasting, the Holt Winters Multiplicative model of Nominal GDP with a MAPE value of 4,767535% is the best model because it has an accuracy value of <10%. While the Holt Winters Additive model of Real GDP with a MAPE value of 4,42387% is also the best model because it has an accuracy value of <10%. }, issn = {2339-2541}, pages = {219--229} doi = {10.14710/j.gauss.13.1.219-229}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/39095} }
Refworks Citation Data :
A country's economic growth will be seen as having grown better or worse than in the past by measuring based on the increase in Gross Domestic Product (GDP). The pattern of Indonesian GDP from 2010 to 2022 shows that the data increases from year to year and there are seasonal fluctuations in the quarter. Holt Winters method is part of the Exponential Smoothing method used for forecasting if the data shows a trend and seasonality in the data pattern. The Holt Winters method has two models, namely additive and multiplicative. Holt Winters Additive is used if the data shows trends and seasonal patterns remain constant. Multiplicative Holt Winter is used if the data shows trends and seasonal patterns proportional to the average rate of the seasonal time series. The data used in this study are GDP Based on Current Prices (Nominal GDP) and GDP on the Basis of Constant Prices (Real GDP). Based on the evaluation of model performance using test data forecasting, the Holt Winters Multiplicative model of Nominal GDP with a MAPE value of 4,767535% is the best model because it has an accuracy value of <10%. While the Holt Winters Additive model of Real GDP with a MAPE value of 4,42387% is also the best model because it has an accuracy value of <10%.
Note: This article has supplementary file(s).
Article Metrics:
Last update:
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Jurnal Gaussian and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Gaussian journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Jurnal Gaussian]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Rukun Santoso (Editor-in-Chief) Editorial Office of Jurnal GaussianDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: jurnalgaussian@gmail.com
Jurnal Gaussian by Departemen Statistika Undip is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Visitor Number:
View statistics
