Article Per Year (5 Year)
p-Index From 2021 - 2026
1.817
P-Index
This Author published in this journals
All Journal Science and Technology Indonesia Journal of Community Service and Empowerment Jurnal Pengabdian Masyarakat Tapis Berseri Jurnal Dedikasi untuk Negeri Abdi Akommedia : Jurnal Pengabdian Masyarakat Jurnal Abdimas Multidisiplin Jurnal Bisantara Informatika Integra: Journal of Integrated Mathematics and Computer Science
Russel, Edwin
Unknown Affiliation
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Science and Technology Indonesia

 1 
Modeling and Analysis Data Production of Oil, and Oil and Gas in Indonesia by Using Threshold Vector Error Correction Model Widiarti; Usman, Mustofa; Putri, Almira Rizka; Russel, Edwin
Science and Technology Indonesia Vol. 9 No. 1 (2024): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2024.9.1.189-197

Abstract

Data in the fields of finance, business, economics, agriculture, the environment and weather are commonly in the form of time series data. To analyze time series data that involves more than one variable (multivariate), vector autoregressive (VAR) models, vector autoregressive moving average (VARMA) models are generally used. If the variables discussed have cointegration, then the VAR model is modified into a vector error correction model (VECM). The relationship between short-term dynamics and deviation in the VECM model is assumed to be linear. If there is a nonlinear relationship between short-term dynamics and deviation, then a threshold vector error correction model (TVECM) can be used. The variables used in this research consist of oil production and Indonesian oil and gas production from January 2019 to March 2021. The research results show that the best model for data on oil production and oil and gas production is the TVECM 2 Regime model. Based on the TVECM 2 Regime model, further analysis, namely Granger causality and Impulse Response Function are discussed.
Modeling Vector Error Correction with Exogeneous (VECMX) Variable for Analyzing Nonstationary Variable Energy Used and Gross Domestic Product (GDP) Usman, Mustofa; Wamiliana; Russel, Edwin; Kurniasari, Dian; Widiarti; Elfaki, Faiz A.M
Science and Technology Indonesia Vol. 10 No. 1 (2025): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.1.283-293

Abstract

Analysis of energy used, GDP and population has been carried out in many countries and has become a topic of interest for many researchers and governments. This is because energy used is an important factor for society and industry in a country. In this study, the modeling of the relationship between energy used, GDP and population as an exogenous variable for the cases of Indonesia from 1967-2023 will be discussed. The energy used and GDP data are nonstationary with order one, I(1), and there is cointegration between energy used and GDP. Therefore, the model which will be used is the Vector Error Correction Model with Exogenous variable (VECMX) with population as the exogenous variable. From the results of analysis, the best model is VECMX(3,1) with cointegration rank R=1. Based on this model, the pattern of the relationship among the three variables, Granger-causality between energy used and GDP, exogenous impact on energy used and GDP, and forecasting for the next 10 years will be discussed.
Dynamic Modeling of Energy Data: World Crude Oil and Coal Prices 2017-2023 (A State-Space Model Analysis of Multivariate Time Series) Russel, Edwin; Wamiliana; Usman, Mustofa; Elfaki, Faiz AM; Adnan, Arisman; Lindrianasari
Science and Technology Indonesia Vol. 10 No. 4 (2025): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.4.1301-1311

Abstract

The analysis of global crude oil and coal prices has attracted considerable research interest, as these prices significantly affect both society and industry, making the topic highly relevant for governments and policy makers. This study examines the correlation between global coal and crude oil prices from 2017 to 2023. It analyzes the behavior of these price series using a unit root test and develops an optimal model for conducting a Granger-causality analysis. To forecast crude oil and coal prices for the next 30 periods, a state-space modeling approach is applied. The unit root test results reveal that these prices are non-stationary, suggesting that any shocks to prices will have persistent effects. The best-fitting model for the association between coal and crude oil prices is a vector autoregressive model of order two (VAR(2)). The Granger-causality results reveal that current crude oil prices are influenced by both their own past values and previous coal prices, and vice versa. Forecasts using the state-space model suggest a modest upward trend for crude oil prices over the next 30 periods, while coal prices are projected to rise more strongly.
Modeling Cointegrated Nonstationary Air Pollution Data: A Forecasting Study of NO₂ and SO₂ in Indonesia (1950–2022) Adnan, Arisman; Erda, Gustriza; Wamiliana; Russel, Edwin
Science and Technology Indonesia Vol. 11 No. 1 (2026): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.1.161-173

Abstract

 Air pollution from nitrogen dioxide (NO2) and sulfur dioxide (SO2) poses serious threats to human respiratory health and contributes to environmental degradation through acid rain formation. In Indonesia, despite rapid industrialization and increasing emissions, studies examining the interrelated dynamics between NO2 and SO2 at the national level remain limited, with most research focusing only on provincial areas and short time periods. This study fills this gap by analyzing the dynamic relationship between NO2 and SO2 using comprehensive national-level time series data from 1950 to 2022. The analysis examines short-term adjustments, long-term equilibrium patterns, directional causality, and shock responses between the two pollutants. The analysis focuses on identifying the best statistical model to capture the interaction between the two variables. Granger causality tests, impulse response functions (IRFs), and forecast error variance decomposition are applied to examine causal links and response dynamics. The data exhibits nonstationary but cointegrated with rank r=1, indicating a long-run equilibrium correlation between two pollutants. Consequently, the Vector Error Correction Model, VECM(4), is selected as the most appropriate model. The study also provides 10-year forecasts for both pollutants insights into potential future air pollution trends in Indonesia, with NO2 rising from 5.29 to 8.09 million tons and SO2 from3.38 to 5.10 million tons, underscoring the urgent need for integrated emission control policies that address both pollutants simultaneously rather than in isolation.
Co-Authors Ambya, Ambya Andrade Paskalia M, Felicia Arisman Adnan Daoud, Jamal I. Dian Kurniasari Elfaki, Faiz A.M Elfaki, Faiz AM Gustriza Erda Lindrianasari Mega Sapitri, Nonik Mustofa Usman Putri, Almira Rizka Putri, Lidya Ayuni Ragayu, Noti Sawitri, Riza Sudrajat Sudrajat Syafis, Kamadie Sumanda W, Wamiliana W, Warsono Wamiliana Widiarti Widiarti Widiarti Widiyanti, Ade