Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Dloifur Rohman Alghifari
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimasi Fuzzy Time Series Menggunakan Algoritme Particle Swarm Optimization Untuk Peramalan Produk Domestik Bruto (PDB) Indonesia Dloifur Rohman Alghifari; Bayu Rahayudi; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (785.752 KB)

Abstract

As one of the input indicators for development programs. This Gross Domestic Product (GDP) forecasting is expected to provide information about economic growth and performance in Indonesia. Data sources of GDP usually come from survey results or from administrative records from various institutions. Sometimes the source data is incomplete or not available when calculating GDP values, it must be determined how to calculate the GDP value so that it can be used to estimate GDP forecasting using fuzzy time series. To improve forecasting accuracy, we use fuzzy time series optimization intervals using particle swarm optimization (PSO). Based on the parameters obtained with a dimension length of 40, many particles of 40, 450 for maximum iteration, the value of c1 and c2 is equal to 1.5 and for inertial weight of 0.3, the forecasting error rate generated using MAPE is 2.48% of the 10 test data. These results indicate good forecasting ability with a low error rate. The comparison of forecasting results for the proposed method is slightly better than the fuzzy time series method with the determination of the average interval based on MAPE 2.66%. But it is no better than the linear regression method with MAPE 1.52%
Co-Authors Bayu Rahayudi Candra Dewi