Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi
Agus Dwi Sulistyono, Agus Dwi
Unknown Affiliation
Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Hybrid Model GSTAR-SUR-NN For Precipitation Data Sulistyono, Agus Dwi; Nugroho, Waego Hadi; Fitriani, Rahma; Iriani, Atiek
CAUCHY Vol 4, No 2 (2016): CAUCHY
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (701.925 KB) | DOI: 10.18860/ca.v4i2.3490

Abstract

Spatio-temporal model that have been developed such as Space-Time Autoregressive (STAR) model, Generalized Space-Time Autoregressive (GSTAR), GSTAR-OLS and GSTAR-SUR. Besides spatio-temporal phenomena, in daily life, we often find nonlinear phenomena, uncommon patterns and unidentified characteristics of the data. One of current developed nonlinear model is a neural network. This study is conducted to form a hybrid model GSTAR-SUR-NN to develop spatio-temporal model that has better prediction. This research is conducted on ten-daily rainfall data at 2005 - 2015 for Blimbing, Singosari, Karangploso, Dau, and Wagir region. Based on the results of this research, indicated that the accuracy of GSTAR ((1), 1,2,3,12,36)-SUR model used cross-covariance weight has relatively similar to GSTAR ((1), 1,2,3 , 12.36)-SUR-NN (25-14-5) for  Blimbing and Singosari region with 5% error level. While Karangploso, Dau, and Wagir, GSTAR ((1), 1,2,3,12,36)-SUR-NN (25-14-5) model has better accuracy in predicting the precipitation at three locations with the value of R2prediction for each location is 0.992, 0.580, and 0.474.
Cross-Covariance Weight of GSTAR-SUR Model for Rainfall Forecasting in Agricultural Areas Sulistyono, Agus Dwi; Hartawati, Hartawati; Suryawardhani, Ni Wayan; Iriany, Atiek; Iriany, Aniek
CAUCHY Vol 6, No 2 (2020): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1009.782 KB) | DOI: 10.18860/ca.v6i2.7544

Abstract

The use of location weights on the formation of the spatio-temporal  model contributes to the accuracy of the model formed. The location weights that are often used include uniform location weight, inverse distance, and cross-correlation normalization. The weight of the location considers the proximity between locations. For data that has a high level of variability, the use of the location weights mentioned above is less relevant. This research was conducted with the aim of obtaining a weighting method that is more suitable for data with high variability. This research was conducted using secondary data derived from 10 daily rainfall data obtained from BMKG Karangploso. The data period used was January 2008 to December 2018. The points of the rain posts studied included the rain post of the Blimbing, Karangploso, Singosari, Dau, and Wagir regions. Based on the results of the research forecasting model obtained is the GSTAR ((1), 1,2,3,12,36) -SUR model. The cross-covariance model produces a better level of accuracy in terms of lower RMSE values and higher R2 values, especially for Karangploso, Dau, and Wagir areas.
Spatio Temporal Modelling for Government Policy the COVID-19 Pandemic in East Java Iriany, Atiek; Aini, Novi Nur; Sulistyono, Agus Dwi
CAUCHY Vol 6, No 4 (2021): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v6i4.10639

Abstract

COVID-19 has cursorily spread globally. Just in four months, its status altered into a pandemic. In Indonesia, the virus epicenter is identified in Java. The first positive case was identified in West Java and later spread in all Java. The Large-scale Social Restrictions are seemingly inefficient as the SARS-CoV-2 transmission remains. As such, the government is struggling to find anticipatory policies and steps best to mitigate the transmission. In this particular article, we used a Spatio-temporal model method for the total COVID-19 cases in Java and forecasted the total cases for the next 14 days, allowing the stakeholders to make more effective policies. The data we were using were the daily data of the cumulative number of COVID-19 cases taken from www.covid19.go.id. Data modelling was conducted using a generalized spatio-temporal autoregressive model. The model acquired to model the COVID-19 cases in Java was the GSTAR(1)(1,0,0) model.
Co-Authors Aniek Iriany Atiek Iriani, Atiek Atiek Iriany Hartawati, Hartawati Novi Nur Aini, Novi Nur Rahma Fitriani Suryawardhani, Ni Wayan Waego Hadi Nugroho