Authors:

Laura Liokelly Toron, Yudi Setyawan, Noviana Pratiwi

Abstract:

“Abstract: Gross Regional Domestic Product is the total number of goods and services produced by production units of all economic sectors of a particular region during one year. BPS NTT noted that the economic growth rate of NTT in 2020 experienced a contraction of -0.83% from 5.24% in the previous year, so this study aims to predict NTT’s GRDP using the ELM method and Holt’s Double Exponential Smoothing. ELM is an artificial neural network that has one hidden layer that is applied through training and testing process, then involves a binary sigmoid activation function and a Moore Penrose Pseudo Inverse matrix to get the output weight used to predict. DES Holt is a forecasting method that pays attention to trend data plots and uses two parameters in its calculations. The results of the forecasting research show that the ELM method with a proportion of 80%:20% is the best method for predicting the GRDP of NTT. The ELM method produces quarterly GRDP values in 2021, which are 17493.19754, 18154.80753, 18712.02153, and 18822.97416 (billion rupiah) with 4 input neurons, 12 hidden layer neurons, 1 output neuron and the MAPE value is 0.7968% which is smaller than DES Holt.”

Keywords

Keyword Not Available

Downloads:

Download data is not yet available.

References

References Not Available

PDF:

https://jurnal.harianregional.com/jmat/full-85200

Published

2022-11-14

How To Cite

TORON, Laura Liokelly; SETYAWAN, Yudi; PRATIWI, Noviana. Perbandingan Extreme Learning Machine dan Double Exponential Smoothing Untuk Meramalkan PDRB di Provinsi NTT.Jurnal Matematika, [S.l.], v. 12, n. 1, p. 34-48, nov. 2022. ISSN 2655-0016. Available at: https://jurnal.harianregional.com/jmat/id-85200. Date accessed: 08 Jul. 2024. doi:https://doi.org/10.24843/JMAT.2022.v12.i01.p147.

Citation Format

ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 12 No 1 (2022)

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License